# A Wireless, Multicolor Fluorescence Image Sensor Implant for Real-Time
Monitoring in Cancer Therapy

Micah Roschelle*, , Rozhan Rabbani*, Surin Gweon, Rohan Kumar, Alec
Vercruysse, Nam Woo Cho, Matthew H. Spitzer, Ali M. Niknejad, Vladimir M.
Stojanović, Mekhail Anwar This work was supported by the Office of the
Director and the National Institute of Dental and Craniofacial Research of the
National Institutes of Health under Award DP2DE030713 and the John V. Carbone
Jr. Pancreatic Cancer Research Memorial Fund. (Corresponding authors: Micah
Roschelle and Mekhail Anwar.) *Equally contributing authors. Micah Roschelle,
Rozhan Rabbani, Surin Gweon, Rohan Kumar, Alec Vercruysse, Ali Niknejad, and
Vladimir Stojanović are with the Department of Electrical Engineering and
Computer Sciences, University of California at Berkeley, Berkeley CA 94720
USA. (email<EMAIL_ADDRESS>Nam Woo Cho is with the Department
of Radiation Oncology and the Department of Otolaryngology-Head and Neck
Surgery, University of California, San Francisco, CA 94158 USA. Matthew
Spitzer is with the Department of Otolaryngology-Head and Neck Surgery and the
Department of Microbiology and Immunology, University of California, San
Francisco, CA 94158 USA. Mekhail Anwar is with the Department of Electrical
Engineering and Computer Sciences, University of California at Berkeley,
Berkeley, CA 94720 USA and also the Department of Radiation Oncology,
University of California, San Francisco, CA 94158 USA. (email:
<EMAIL_ADDRESS>mekhail.anwar@ucsf.edu).

###### Abstract

Real-time monitoring of dynamic biological processes in the body is critical
to understanding disease progression and treatment response. This data, for
instance, can help address the lower than 50% response rates to cancer
immunotherapy. However, current clinical imaging modalities lack the molecular
contrast, resolution, and chronic usability for rapid and accurate response
assessments. Here, we present a fully wireless image sensor featuring a 2.5×5
mm2 CMOS integrated circuit for multicolor fluorescence imaging deep in
tissue. The sensor operates wirelessly via ultrasound (US) at 5 cm depth in
oil, harvesting energy with 221 mW/cm2 incident US power density (31% of FDA
limits) and backscattering data at 13 kbps with a bit error rate <10-6. In-
situ fluorescence excitation is provided by micro-laser diodes controlled with
a programmable on-chip driver. An optical frontend combining a multi-bandpass
interference filter and a fiber optic plate provides >6 OD excitation blocking
and enables three-color imaging for detecting multiple cell types. A
36×40-pixel array captures images with <125 µm resolution. We demonstrate
wireless, dual-color fluorescence imaging of both effector and suppressor
immune cells in ex vivo mouse tumor samples with and without immunotherapy.
These results show promise for providing rapid insight into therapeutic
response and resistance, guiding personalized medicine.

###### Index Terms:

Biomedical implant, fluorescence imaging, ultrasound energy harvesting,
immunotherapy, personalized medicine.

##  I Introduction

WIRELESS , miniaturized, implantable sensors can monitor intricate biological
processes unfolding in the body in real-time. Typically accessible only
through highly invasive techniques, this data is crucial for advancing
personalized medicine, tailoring treatments to individual responses to address
the wide heterogeneity in therapeutic outcomes among patients.

One meaningful application is monitoring tumor response to cancer
immunotherapy, a promising treatment that unlocks the patient’s own immune
system to fight cancer. For instance, immune checkpoint inhibitors (ICIs), a
class of immunotherapy, have been shown to nearly double patient survival
rates in melanoma [1] and metastatic lung cancer [2] with a lower incidence of
adverse effects compared to conventional treatments like chemotherapy [3].
While more than 40% of US cancer patients are estimated to be eligible for
ICIs [4], these therapies face a significant challenge: across most cancer
types, less than 30% of patients respond to treatment [5, 6]. For non-
responders, time spent on ineffective therapies not only allows for their
cancer to grow and spread, but also exposes them to unnecessary toxicity with
high-grade adverse events rates often exceeding 10% [5] and financial burdens
of more than $150,000 per year [7, 8]. Rapid assessments of therapeutic
response that also provide insight into the underlying mechanisms of
resistance can help clinicians quickly identify non-responders and pivot to
more effective second-line therapies to overcome resistance. However, such an
assessment must capture the complex and dynamic interplay between various
effector and suppressor immune cells and cancer that determines response [5].

Current clinical imaging falls short of this goal. Anatomical imaging
modalities such as computed tomography (CT) and magnetic resonance imaging
(MRI) capture changes in tumor size, which take months to manifest and do not
reliably correlate with response [9]. These limitations are apparent in
standard response criteria. For example, iRECIST defines a partial response as
at least a 30% reduction in tumor dimensions with a minimum size of 1 cm and
recommends confirmation of disease progression at long 4–8 week intervals
[10], [11]. Alternatively, positron emission tomography (PET) can image the
underlying biology with molecular contrast [12], but is fundamentally limited
to imaging a single cell type or biomarker [13] at millimeter-scale resolution
[14]. As the immune response depends on interactions between a variety of
immune cells, it cannot be reliably predicted by a single biomarker [15, 16].
Moreover, this millimeter-scale resolution averages out the spatial
distributions of different cell populations within the tumor, shown to be
increasingly important in understanding therapeutic resistance [17, 18].

Fluorescence microscopy, on the other hand, provides multi-cellular resolution
across multiple biomarkers, essential to visualizing a more complete picture
of the immune response. In fluorescence microscopy, targeted cells are labeled
with fluorescent dyes, or fluorophores, which absorb light near a specific
wavelength and emit light at slightly longer wavelengths [19]. Multiple cell
types can be imaged simultaneously by labeling each with a different color
fluorophore. However, in vivo optical imaging is constrained by scattering in
tissue which fundamentally limits the penetration depth of light in the body
to a few millimeters, even at near-infrared (NIR) wavelengths where tissue
absorption is minimal and scattering is reduced [20]. Therefore, chronic
fluorescence imaging at depth requires implantable imagers with integrated
light sources providing in-situ illumination.

Fluorescence imagers can be miniaturized to the scale of a single chip by
eliminating bulky lenses through contact imaging [21, 22, 23, 24, 25]. To this
end, prior work has demonstrated on-chip or in-package integration of focusing
optics [21, 26] as well as fluorescence filters [22, 23, 24, 25, 27] and light
sources [24]. However, these systems are wired, precluding long-term
implantation without risk of infection. While a fluorescence sensor with
wireless radio-frequency (RF) communication is presented in [23], it uses a
centimeter-scale battery for power and lacks wireless charging. Both wireless
power transfer and communication are necessary for chronic use of these
devices.

Here we present a fully wireless, miniaturized fluorescence image sensor
capable of three-color fluorescence imaging, aiming to enable real-time,
chronic monitoring of cellular interactions deep in the body (Fig. 1). Wired
connections and batteries are eliminated by power harvesting and bi-
directional communication through ultrasound (US). Among wireless power
transfer modalities such as near-field inductive coupling, RF, and optical, US
offers low loss in tissue (0.5–1 dB/MHz/cm [28]), a high Food and Drug
Administration (FDA) regulatory limit for power density (720 mW/cm2), and a
short wavelength (~3–4 mm in the PZT material at 1 MHz) enabling power
transfer to millimeter-scale implants at centimeter-scale depths [29, 30].

While significant progress toward a wireless fluorescence imaging system using
US is presented in our prior work [31, 32, 33], this system has several
limitations. It incorporates a large (0.18 cm3) ~1 mF off-chip capacitor for
energy storage. It only operates at 2 cm depth, constraining its application
to superficial tumors while exceeding FDA US safety limits by 26% due to high
acoustic power requirements. Moreover, the sensor only images a single
fluorescent channel, lacking the necessary hardware for multicolor imaging
such as a wirelessly programmable laser driver to control multiple excitation
lasers and a multi-bandpass optical filter. Additionally, due to in-pixel
leakage during readout, the sensitivity of the imager when operating
wirelessly is limited to high concentrations of fluorophores, rendering it
insufficient for imaging biologically relevant samples.

Figure 1: Concept of a fully wireless, multicolor, implantable imager for
real-time monitoring of immune response.

This work demonstrates a new system with significant improvements in
performance and size, specifically designed for multicolor imaging. Our new
system shows fully wireless operation at 5 cm depth in oil, requiring 221
mW/cm2 US power flux density (31% of FDA limits) for power harvesting and
transmitting data with a bit error rate (BER) less than 10-6 through US
backscatter. It powers three different-wavelength laser diodes programmed
through US downlink and incorporates a multi-bandpass optical frontend
expanding on the design in [34] to enable three-color fluorescence imaging.
Moreover, we illustrate the application of our sensor in assessing response to
cancer immunotherapy through multicolor fluorescence imaging of both effector
and suppressor immune cells in ex vivo mice tumor samples with and without
immunotherapy. Finally, a proof-of-concept mechanical assembly demonstrates a
small form factor of 0.09 cm3.

This article further explains and expands on the work presented in [35] and is
organized as follows. Section II discusses the components and design
specifications for a fully wireless, multicolor fluorescence imager. We
describe the design and implementation of our system in Section III. Section
IV presents system-level measurement results. We illustrate the application of
our sensor with ex vivo imaging results in Section V. Finally, Section VI
includes a comparison with the state of the art and the conclusion.

##  II System Overview

Fig. 2 shows a diagram and mechanical assembly of the full system on a flex
PCB with all external components. The system consists of: 1) micro-laser
diodes (µLDs) for in-situ illumination; 2) an optical frontend comprising of a
fiber optic plate and a multi-bandpass interference filter for lens-less
multicolor fluorescence imaging; 3) a piezoceramic as the US transceiver; 4)
off-chip capacitors for energy storage; and 5) an ASIC to integrate all of
this functionality. In this section, we will describe the design of the
components in the system and derive design requirements for the ASIC.

Figure 2: (a) To-scale diagram of the full system. (b) Mechanical assembly.

###  II-A Multicolor Fluorescence Imaging

Fig. 3 illustrates the principle of multicolor fluorescence imaging. The
fluorophores are first conjugated to a probe (Fig. 3(a)), such as an antibody,
targeted toward a cell type of interest [19]. For in vivo imaging, the
conjugated probe can be administered systemically through intravenous
injection, binding only to targeted cells. Many organic fluorophores have low
toxicity at doses relevant for imaging [36] and a number of fluorescent probes
are FDA-approved or in clinical trials, including some using Fluorescein (FAM)
and Cyanine5 (Cy5) [37], the fluorophores in our ex vivo studies. Once
injected, the half-life of antibody-based probes is days to weeks [38] and
free-floating unbound probes are cleared through the liver and kidneys in 1–7
days [39].

Figure 3: Multicolor fluorescence imaging. (a) Each cell type is labeled with
a different color fluorescent probe. (b,c) Fluorophores are excited near the
absorption peak and emit light at a slightly longer wavelength. A multi-
bandpass filter passes emissions while blocking excitation.

After labeling the cells, the fluorophores are excited near their absorption
peak ($\lambda$EX) and emit light at a slightly longer wavelength with a peak
at $\lambda$EM (Fig. 3(b) and (c)). For organic fluorophores, the difference
between the absorption and emission peaks, or Stokes shift, is 10–30 nm (26 nm
for FAM and 18 nm for Cy5). Moreover, due to the small absorption cross-
section of the fluorophores relative to the illuminated field of view (FoV),
the excitation light is often 4 to 6 orders of magnitude stronger than the
emission light. Thus, in order to detect the weak fluorescence signal, an
optical filter with an optical density (OD) $\geq$ 6 is required to attenuate
out-of-band excitation light that would otherwise saturate the sensor.
Avoiding a filter altogether through time-gated imaging [24, 40, 41]—where
excitation and imaging are separated in the time domain—leads to inadequate
excitation rejection and low signal intensities with typical organic
fluorophores, which have fluorescence lifetimes less than 10 ns [42].
Moreover, background subtraction in the electrical domain [22] adds additional
noise sources and is challenging in vivo as the excitation background is
dependent on tissue scattering.

For multicolor imaging, a variety of organic fluorophores are available with
absorption and emission wavelengths spanning the visible and NIR spectrum
[43]. Their narrow absorption and emission spectra allow for multiplexed
imaging using a monochrome sensor by taking a separate image at each
excitation wavelength. Therefore, multicolor fluorescence imaging requires
multiple excitation sources and a multi-bandpass filter to block all
excitation wavelengths while passing fluorescence emissions.

###  II-B Light Sources

For fluorescence excitation, we use µLDs with wavelengths of 650 nm
(250×300×100 µm3, CHIP-650-P5, Roithner LaserTechnik GmbH) and 455 nm
(120×300×90 µm3, LS0512HBE1, Light Avenue). A third 785 nm laser diode
(L785P5, ThorLabs) in a TO-can package is used for proof-of-principle three-
color fluorescence imaging and will be replaced by a µLD in the future. Laser
diodes are chosen instead of LEDs which have broader spectral bandwidths that
can overlap with fluorescence emissions. These out-of-band emissions
necessitate excitation filters on the LEDs that complicate sensor design and
waste optical power output [44].

Fig. 4(a) and (b) show the measured power-current-voltage (PIV) curves for all
three lasers and their calculated wall-plug efficiencies
($P_{Optical}/P_{Electrical}$), respectively. The lasers have different
forward voltages: ~2 V for the 650 nm and 785 nm lasers and ~4.5 V for the 455
nm laser. Because of their several-mA threshold currents, the lasers operate
most efficiently near their maximum current ratings. These characteristics
motivate the design of a laser driver with programmable current that is
tolerant of a wide range of forward voltages.

Figure 4: Measured laser diode (a) PIV curves and (b) wall-plug efficiencies.

###  II-C Optical Frontend Design

The optical frontend design builds on our prior work [34] and consists of a
multi-bandpass interference filter and a low-numerical-aperture fiber optic
plate (FOP). Interference filters offer more-ideal filter characteristics than
absorption filters [27] or CMOS metal filters [22, 23, 45], which do not allow
for optimal excitation and imaging of organic fluorophores due to their
gradual cutoff transitions, weak out-of-band attenuation, and significant
passband losses. Hybrid filters combining interference and absorption filters
[24, 25, 46] retain the poor passband characteristics of absorption filters.
Another major advantage of interference filters is their ability to support
multiple passbands across the visible and NIR spectra for multicolor imaging.
In contrast, demonstrated dual-color fluorescence sensors with absorption or
CMOS filters rely on dedicated pixels for each color [22, 47, 48, 49],
reducing the sensor sensitivity and resolution.

However, interference filters are sensitive to angle of incidence (AOI) [50].
At increasing AOIs, the filter passbands shift towards shorter wavelengths,
eventually transmitting the excitation light. This property is problematic for
lensless imaging where the AOI is not precisely controlled and the excitation
light is often angled between the sensor and the tissue above it. To mitigate
this effect, the FOP acts as an angle filter, blocking off-axis excitation
light that would otherwise pass through the filter. The FOP also improves
resolution by eliminating divergent fluorescent emissions that contribute to
blur, albeit at the cost of reducing the overall collected signal.

Here, we expand the dual-bandpass design in [34] to three-color fluorescence
imaging with a new interference filter. Fig. 5(a) shows the normal incidence
(AOI=0°) transmittance spectra of the filter (ZET488/647/780+800lpm, Chroma
Technologies Corp) which has three passbands with greater than 93% average
transmittance. The first two bands pass the emissions of FAM and Cy5, the
fluorophores used in our ex vivo imaging studies. The 800 nm band, added in
this work, provides another fluorescence channel in the NIR-I window (700–900
nm), a preferred region for in vivo imaging where tissue scattering,
absorption, and autofluorescence are minimal compared to the visible spectrum
(400–700 nm) [51, 52]. At normal incidence, the filter provides sufficient
blocking of the lasers: more than 6 OD attenuation at both 450 nm and 650 nm
as well as more than 5 OD attenuation at 785 nm.

Figure 5: (a) Normal incidence transmittance spectra of the multi-bandpass
interference filter. (b,c) Measured transmittance through the FOP across AOIs.
(d) Angular transmittance of the filter with and without the FOP measured at
the excitation laser wavelengths.

The 500 µm-thick FOP (LNP121011, Shenzhen Laser, LTD) consists of a matrix of
10 µm optical fibers embedded in black, absorptive glass. It has a normal
incidence transmittance of 35% and a full-width at half maximum (FWHM) of 10°
at 455 nm, which both reduce at longer wavelengths as shown in Fig. 5(b). The
angular transmittance measurements in Fig. 5(c) show that beyond an AOI of 35°
the FOP provides more than 6 OD attenuation of all three lasers.

Fig. 5(d) shows the transmittance through the filter with and without the FOP
across different AOIs measured at the excitation wavelengths using collimated,
fiber-coupled lasers. The filter attenuation at AOI=0° is different from that
in Fig. 5(a) due to out-of-band emissions from the lasers. While the filter
blocks the excitation lasers near 0°, the laser transmittance rapidly
increases beyond AOIs of 20° for 650 nm and 785 nm and 60° for 455 nm.
However, with the FOP, the optical frontend provides more than 6 OD of
attenuation of all excitation lasers at AOIs greater than 5°. The maximum
measured attenuation is limited by the sensitivity of the power meter (PM100D
with S120C Photodiode, Thorlabs) used for this measurement.

For fabrication, the interference filter is directly deposited on the FOP,
resulting in a total thickness of approximately 510 µm. The optical frontend
is fixed to the chip using optically transparent epoxy (SYLGARD 184, Dow
Chemicals). The filter is placed in between the chip and the FOP to ensure
that it blocks any excitation light scattered through the FOP [34].

###  II-D Ultrasound Link

We use a 1.5×1.5×1.5 mm3 piezoceramic (lead zirconate titanate) as the US
transceiver for wireless power transfer and bi-directional communication. The
thickness of the piezo is directly proportional to the harvested voltage and
inversely proportional to the operation frequency [29]. Therefore, we chose a
thickness of 1.5 mm to balance minimizing the overall size of the piezo with
the need for harvesting a high enough voltage to drive the lasers while
operating at a lower frequency with less tissue attenuation. An aspect ratio
of one is selected as a compromise between volumetric efficiency and
backscattering amplitude, as outlined in [53]. The piezo is mounted on a flex
PCB for testing (Fig. 6(a)). On the backside of the piezo, an air gap is
created by covering a through-hole via with a 3D-printed lid. The air gap
reduces the acoustic impedance of the backside medium from 1.34 MRayl in
canola oil to ~0 MRayl in air, decreasing the electrical impedance of the
piezo to improve the power transfer efficiency [54].

Figure 6: (a) Piezo assembly with the air gap. (b) Measured electrical
impedance of the piezo across frequency. (c) Measured harvested voltage across
frequency with the piezo in open circuit condition and loaded by the chip.

Fig. 6(b) shows the impedance spectrum of the piezo measured within canola
oil. Canola oil has 0.075 dB/cm acoustic attenuation at 920 kHz and 1.34 MRayl
acoustic impedance [55] similar to the impedance (1.4–1.67 MRayl) of tissue
[28]. The series and parallel resonance frequencies of the piezo occur at,
fS=894 kHz and fP=960 kHz, respectively. Fig. 6(c) shows the normalized
harvested voltage across frequency when the piezo is open circuit condition
and when it is loaded with the chip (see section IV.A for the setup). While
operating near fS minimizes the impedance, the open circuit voltage is
maximized near fP. Therefore, the maximum harvested voltage with the chip
occurs between fS and fP at 920 kHz.

###  II-E System Design Considerations

To derive the required harvested energy per image for sizing the storage
capacitor, we estimate the signal detected by a pixel from Cy5-labeled CD8+
T-cells, a type of immune cell imaged in our ex vivo studies. The total
emitted optical power, $P_{CELLS}$, from $C$ fluorescently labeled cells as a
function of the input excitation intensity, $I_{IN}$, is given by

$P_{CELLS}=C\cdot N_{FL}\cdot\sigma\cdot QY\cdot I_{IN}.$ (1)

$N_{FL}$ is the number of fluorophores bound to each cell. Typically, between
0.5–2.1×106 CD8+ antibodies bind to a single CD8+ T-cell [56] with each
antibody containing 2–8 fluorophores [57]. $\sigma$ and $QY$ are the
absorption cross-section and quantum yield of the fluorophore, respectively
(9.55×10-16 cm2 and 20% for Cy5 [58]). We assume that a single pixel (with 55
µm pitch in our design) subtends a FoV containing $C=100$ T-cells, considering
that a T-cell is 5–10 µm in diameter [59]. Assuming that the 650 nm µLD
uniformly illuminates the FoV of our sensor (2×2.2 mm2) and outputs 10 mW of
optical power at ILD=20 mA bias (see Fig. 4), $I_{IN}$ is approximately 223
mW/cm2. Therefore, the estimated total fluorescence signal is 20 nW. This
signal can be converted to the expected photodiode current, $I_{PH}$,
according to

$I_{PH}=P_{CELLS}\cdot\frac{A_{PIXEL}}{4\pi z_{DIST}^{2}}\cdot(1-L_{FOP})\cdot
R.$ (2)

This equation accounts for both the spreading loss over the $z_{DIST}\approx
500$ µm distance to the pixel with area, $A_{PIXEL}$ (44×44 µm2 in our design)
and the insertion loss of the FOP, $L_{FOP}$ (~75% at 650 nm). Given that the
pixel has a responsivity, $R$, of 0.21 A/W at 650 nm, we expect $I_{PH}$ on
the order of 6.3 fA.

In the capacitive trans-impedance amplifier (CTIA)-based pixel architecture
reused from [21] the photocurrent is sensed by integrating it on a capacitor,
$C_{INT}$, during the exposure time, $T_{EXP}$, resulting in a pixel output
voltage of

$V_{PIXEL}=\frac{I_{PH}\cdot T_{EXP}}{C_{INT}}.$ (3)

Sensing the fluorescence signal relies on $V_{PIXEL}$ exceeding the noise
floor, characterized by the signal-to-noise ratio (SNR). Generally, SNR can be
improved by increasing the total imaging time either through a longer exposure
time, $T_{EXP}$, or by averaging multiple images. Following the derivation in
[32], the SNR at the output of a CTIA-based pixel when averaging $n$ images
with an exposure time of $\frac{T_{EXP}}{n}$ is given by

$SNR(n\cdot\frac{T_{EXP}}{n})=\frac{signal}{noise}=\frac{\frac{I_{PH}T_{EXP}}{C_{INT}}}{\sqrt{\frac{T_{EXP}}{C_{INT}^{2}}}2q_{e}i_{D}+n\overline{v_{NR}^{2}}}.$
(4)

This equation enables study of the SNR tradeoff between (1) taking a single
exposure of ($n$=1) and (2) averaging $n$ images with exposures of
$\frac{T_{EXP}}{n}$. The noise has two components: readout noise,
$\overline{v_{NR}^{2}}$, and shot noise from the photocurrent and dark
current, $i_{D}=I_{PH}+I_{DARK}$. $q_{e}$ is the charge of an electron. The
factor of $n$ only appears in the readout noise term. Therefore, if shot noise
is the dominant source of noise, for small $n$, both (1) and (2) result in the
same SNR. However, with increasing and lower exposure time per frame, readout
noise dominates the overall noise of the averaged image, necessitating a
greater number of averages to maintain the same SNR as a single exposure.

Using the estimated $I_{PH}$ and the measured noise values reported in Section
IV, we calculate that without averaging, a $T_{EXP}$ of 98 ms is required to
achieve an SNR of 20 dB (10×). This result corresponds to a minimum required
energy ( $I_{LD}\cdot V_{LD}\cdot T_{EXP}$) of 4.16 mJ per image.

Delivering $I_{LD}$=20 mA from the incident US signal, given a piezo impedance
of 5.4 k$\Omega$ at 920 kHz, requires an open circuit voltage of at least 108
V, which is not practical within FDA limits. Therefore, harvested energy must
first be stored on a capacitor to later supply the lasers when taking an
image. The size of the storage capacitor, $C_{STORE}$, is determined by
$C_{STORE}=\frac{I_{LD}T_{EXP}}{\Delta V_{CSTORE}}$ in order to supply
$I_{LD}$ for the duration of $T_{EXP}$. $\Delta V_{CSTORE}$ is the voltage
drop on the capacitor during $T_{EXP}$. Maximizing $\Delta V_{CSTORE}$ results
in a smaller capacitor size, but is limited by the maximum harvested voltage
and the minimum supply requirements for operating the chip or laser. Assuming
$\Delta V_{CSTORE}$=3 V, results in a capacitor size of 650 µF. Capacitors of
this size are large physical components, increasing implant volume as in [32].
Therefore, the capacitor size can be minimized by reducing the required energy
per image through the averaging strategy discussed previously.

Fig. 7(a) compares the SNR of a pixel with different levels of averaging. The
signal is the estimated photocurrent from the above analysis (6.3 fA) and the
noise is measured with the sensor from dark images (see Fig. 21(c)). Each data
point on the black curve represents an exposure time of TEXPi and a number of
averages ni such that the total exposure time, niTEXPi=96 ms stays constant.
As TEXPi decreases (and ni increases), readout noise dominates the pixel
output noise (because shot noise decreases with lower TEXPi), requiring
additional averages to achieve the same SNR of a single exposure. The orange
curve in Fig. 7(a) shows the increased number of averages, xi > ni, required
to reach an SNR (shown in blue) within 90% of the initial SNR for TEXP=96 ms.
Therefore, using averaging to decrease exposure time for individual frames
increases the overall imaging time to greater than 96 ms. As shown in Fig.
7(b), the capacitor size decreases linearly with lower TEXPi ranging from 640
µF for TEXPi=96 ms to 50 µF for TEXPi=8 ms. Charging such a capacitor through
US takes several seconds to minutes, dominating the frame time (see Section
IV.B). Thus, for small exposure times, the additional required averages can
significantly increase the total imaging time. The total imaging time must be
less than several minutes to capture the motion of immune cells, which have
mean velocities of 10 µm/min in the tumor microenvironment [60].

Figure 7: (a) In black, the SNR of a pixel with the estimated photocurrent and
measured dark noise (see Fig. 21(c)) across different exposure times (TEXP,i)
and averaging ni images such that total imaging time $T_{EXPi}n_{i}=96$ ms
remains constant. In blue, the SNR after $x_{i}$ averages (orange) required to
maintain 90% of the SNR at $T_{EXPi}=96ms$. (b) Capacitor size vs. exposure
time.

Following these guidelines, we chose an 0805 100 µF tantalum capacitor for
CSTORE with a size of 2×1.25×0.9 mm3 (0.002 cm3). This capacitor can supply 20
mA of laser current for TEXP=16 ms while dropping its voltage by 3 V.
Averaging is employed to enhance SNR to levels comparable to those achieved by
longer exposure times. We use a tantalum capacitor as opposed to a ceramic
capacitor, which can lose up to 40–80% of its initial capacitance as the DC
bias voltage increases and reduces the dielectric permittivity [61].

##  III System Design and Implementation

Fig. 8 shows the system block diagram of the ASIC with external connections to
the piezo, off-chip storage capacitors, and µLDs. The ASIC has 4 main
subsystems: (1) power management unit (PMU), (2) digital control, (3) laser
driver, and (4) imaging frontend with readout.

Figure 8: System block diagram.

The PMU consists of an active rectifier for AC-DC conversion of the piezo
signal and a charge pump for generating an up to 6 V supply for driving the
lasers. Harvested energy is stored on two off-chip capacitors, CVCP=10 µF and
CSTORE=100 µF, to separate the power supplies of the lasers from the rest of
the sensor throughout its operation. A PTAT develops current and voltage
references and several low dropout voltage regulators (LDOs) generate stable
DC power supplies for the chip. The sensor is programmed and controlled
through a finite state machine (FSM) with 6 states of operation: charging up
the storage capacitors (Charge-Up); programming the image sensor and laser
driver parameters through US downlink (Set TEXP and Set LD); taking an image
(Imaging); digitizing and storing the image (Readout); and wirelessly
transmitting the data via US backscatter (Backscattering). To take an image,
the laser driver, configured during downlink, supplies a µLD using energy
stored in CSTORE. The image is captured on a 36×40-pixel array. During
Readout, the pixel data is digitized by 4 parallel ADCs to be saved in the
memory. Finally, image data is transmitted by modulating the reflected
amplitude of incident US pulses with the SMOD switch.

The design and operation of the subsystems are described in detail below.

###  III-A Power Management Unit

Fig. 9 shows the schematic of the active rectifier and charge pump. The active
rectifier converts the harvested AC signal on the piezo to a 3 V DC voltage
(VRECT), which is stabilized by a 4.7 nF off-chip capacitor. VRECT is then
multiplied by 1.83× to a 5.5 V supply (VCP) with the cross-coupled charge
pump. The cross-coupled topology is chosen for its high power conversion
efficiency for an optimized input range [62]. Compared to a rectifier-only
architecture used in [32], the charge pump reduces the required harvested AC
voltage on the piezo (VPIEZO) to achieve an output voltage (VCP) of 5.5 V by
1.7×, which results in a 3× lower acoustic power density requirement. Acoustic
power density is a square function of acoustic pressure, which is linearly
proportional to the harvested AC voltage. Therefore, lowering the required
harvested piezo voltage reduces the acoustic power density to ensure operation
within FDA safety limits. However, with this architecture, the overall
charging time increases due to the energy loss from the charge pump.

Figure 9: PMU schematic consisting of (a) a full-wave active rectifier, (b) a
cross-coupled charge pump, and (c) storage capacitors.

During Charge-Up, CVCP and CSTORE are connected through the CSTORE switch and
are charged through the PMU. CSTORE stores energy for the lasers and imager
array and a smaller CVCP stores energy for the readout and digital control.
Following manufacturer guidelines, the external US transducer is duty-cycled
for reduced average power dissipation to prevent damage to it from overheating
while providing enough US power density to achieve sufficient harvested
voltage on the sensor. To minimize power consumption during Charge-Up, the
laser driver, pixel array, readout circuits and memory are switched off. A
diode-based voltage clamp prevents charging beyond 6 V to protect the devices
from overvoltage.

Five LDOs (Fig. S1) regulate the harvested voltage into stable DC power
supplies and are compensated with off-chip 0201 surface mount capacitors
(10–200 nF). They generate reference voltages of 0.5 V and 2.1 V for the ADCs,
separate 1.8 V power supplies for the digital control and for the pixel array
and laser driver biasing, and a 3.3 V supply for the readout.

A PTAT circuit generates a 200 nA reference current, IREF, and 1 V and 0.5 V
references to bias the chip. The PTAT, with schematic shown in Fig. 10, uses a
constant-gm topology to minimize the dependence on threshold voltage process
variation. A PMOS core (M1–M4) avoids the body effect as deep N-well
transistors were not available in the process. The diode-based start-up
circuit (D1–D3) prevents zero current operation. To ensure that generated
references are stable across the large voltage drop on VCP from 5.5 to 3.5 V,
cascode current mirrors with high output impedance are used throughout the
design. The voltage references are buffered and are generated by mirroring
IREF (M3, M4, M9, M10) through resistors R4 and R5.

Figure 10: PTAT schematic.

###  III-B Digital Control

The chip operates according to the system timing diagram shown in Fig. 11.
When VCP reaches 3.9 V, ensuring stable operation of the chip, a power-on
reset (POR, Fig. S2) circuit initializes the FSM. The FSM is synchronized to
the external US transducer by on-off-key modulation of the US envelope, which
is demodulated by a watchdog circuit.

Figure 11: System timing diagram.

The schematic of the watchdog circuit is shown in Fig. 12. A latched-based
control eliminates glitches in detecting the presence of the US pulses within
3 µs of the initial rising edge. The unwanted transitions result from
insufficient drive strength of the AC inputs to transistors M1 and M2 during
the gradual ramp-up of the US pulse.

Figure 12: Schematic of watchdog circuit with error-tolerant edge detection.

To relay timing information to the FSM, the clock is extracted from the US
carrier frequency (920 kHz). An US pulse longer than 1 ms indicates the end of
the Charge-Up state. At this moment, the CSTORE switch is opened to isolate
the storage capacitors, allowing VCSTORE to drop to a minimum of 2.5 V during
Imaging while maintaining VCP above 3.5 V for the 3.3 V readout circuits. This
approach allows for maximum energy usage from CSTORE, resulting in a 33%
smaller required capacitance assuming a 5.5 V Charge-Up voltage.

After Charge-Up, the ASIC is programmed during the Set TEXP and Set LD states.
As shown in Fig. 11, the transmitted downlink data is decoded through time-to-
digital conversion of the US pulse widths. In each state, 4 LSBs are discarded
to account for timing variations in the watchdog signal. In Set TEXP, the
exposure time, TEXP, is set through the 5 MSBs and is programmable from 0–248
ms with LSB=8 ms. The next 2 bits set the pixel reset time, TRST, which can be
100, 200, 500, or 1000 µs. In Set LD, 3 MSBs set the 1-hot encoded laser
channel and the next 5 bits determine the laser current, ILD. On the falling
edge of the watchdog after Set LD, the laser driver and the pixel array bias
circuits are turned on to prepare for Imaging.

###  III-C Laser Driver

Fig. 13 shows the schematic of the 3-channel laser driver with programmable
output current. To minimize the change in driver current, ILD, across the
large voltage drop on VCSTORE (5.5–2.5 V), the driver must have high output
impedance. Therefore, a gain-boosted cascode current source topology is used,
in which the output impedance of the current source (M8–M15) is multiplied by
the 65 dB gain of the cascoded boost amplifier (M4–M7). A 5-bit current DAC
(M11–M15) enables a programmable output current from 0–115 mA with a 3.9 mA
LSB. While the µLDs in this work operate under 40 mA (see Fig. 4), this range
accommodates a variety of commercial µLDs with threshold currents up to 100 mA
for future applications. Since only one laser is turned on at a time, the same
driver circuitry is used for all three lasers. Thus, the cascode transistors
select between the laser channels. For maximum output swing, Vx is set by a
level-shifting diode, M3, to bias M11–M15 at the edge of triode. A headroom of
at least 400 mV is required at the drains of M8–M10 (VLD−) to ensure operation
in saturation.

Figure 13: Schematic of the 3-channel programmable laser driver.

###  III-D Imaging Frontend and Readout

The imaging frontend is similar to that presented in [21], but without the
angle selective gratings as image deblurring is now provided by the FOP. The
image sensor consists of a 36×40 array of pixels with a 44×44 µm2 Nwell/Psub
photodiode and a 55 µm pitch, covering a 2×2.2 mm2 FoV. The pixel
architecture, shown in Fig. 14(a), is based on a CTIA with CINT=11 fF. To
reduce low-frequency noise, reset switch sampling noise, and pixel offset, a
correlated double-sampling scheme is implemented with the following pixel
timing (illustrated in Fig. 14(b)). First, the voltage on CINT is set to zero
during the initial reset phase, TRST, with timing configured in the Set TEXP
state. For the exposure time, TEXP, the photocurrent is integrated on CINT
generating the pixel output voltage, $V_{OUT}=V_{0}+I_{PD}T_{EXP}/C_{INT}$,
which is sampled on reset (CR) and signal (CS) sampling capacitors after
intervals of 100 µs and $T_{EXP}+100$ µs, respectively. The final pixel value
(VPIXEL) is the difference between the signal (VS) and reset (VR) values.

Figure 14: (a) Active pixel architecture with correlated double sampling. (b)
Pixel timing diagram.

After Imaging, the analog pixel values are digitized and stored in memory
during the Readout state. Readout duration is set to limit the leakage on the
in-pixel sampling capacitors to less than an LSB. Therefore, the readout is
performed in parallel across 4 channels each spanning 10-pixel columns. Each
channel consists of an 8-bit differential SAR ADC (Fig. S3) driven by a
buffer. The ADC has a dynamic range of 500 mV with an LSB of 1.95 mV, which is
below the pixel readout noise (see Section III.E). The readout circuits
operate on a 3.3 V supply to ensure sufficient headroom considering that the
in-pixel source followers level-shift the sampled pixel voltages up by 1 V.
Thus, the size of CVCP is chosen to maintain VCP above 3.5 V throughout this
state. The signal (VS) and reset (VR) pixel values are subtracted by the
differential ADCs, and the digitized pixel values are stored immediately after
conversion in a 11.52 kb latched-based memory. Unlike the work in [32], this
design enables a short Readout time of 5.4 ms, which is not limited by the
longer Backscattering state (890 ms at 5 cm depth) that increases with depth
due to the longer time of flight of the acoustic waves.

###  III-E Data Transmission

During Backscattering, the memory is read serially ($\Theta$MOD in Fig. 8) and
transmitted by modulating the amplitude of the reflected (backscattered) US
pulses using a switch (SMOD in Fig. 8). The uplink communication protocol is
shown in the timing diagram in Fig. 11. The transmitted data for each pixel
comprises a 9-bit packet containing a header (set to 0) followed by 8 data
bits. The header pulse allows for a one-pulse delay to make sure memory is
read and loaded into the serializer before data transmission. Additionally,
the header is set to a known value of zero to help identify the backscattered
bit values.

The external transducer generates a sequence of pulses each spanning a few
cycles of the US carrier for the header and 8 individual bits. After a time of
flight (ToF=33 µs for 5 cm depth) the acoustic pulses reach the piezo and
reflect with an amplitude proportional to the reflection coefficient of the
piezo, $\Gamma$. $\Gamma$ is dependent on the electrical impedance loading the
piezo, $R_{LOAD}$ and, therefore, can be controlled through the SMOD switch.
Near the parallel resonance frequency of the piezo, $\Gamma\propto
R_{PIEZO}/(R_{LOAD}+R_{PIEZO})$, where $R_{PIEZO}$ is the equivalent
resistance of the piezo [53]. The SMOD switch impedance can be configured
(hard-coded) by 2 bits to account for different $R_{PIEZO}$ values. After a
second ToF, the backscattered signal is received by the external transducer
and is demodulated to reconstruct the image. To avoid overlap of high voltage
Tx and low voltage reflected Rx pulses, the external transducer transmits 2
bits within 2 ToFs and listens for the next 2 ToFs as shown in Fig. 11.

##  IV Measurement Results

Fig. 15(a) shows the die photo of the chip. The ASIC measures 2.5×5 mm2 and is
fabricated in a TSMC 180 nm high-voltage (1.8/5/32 V) LDMOS CMOS process. 1.8
V transistors are used for the digital, pixel, and laser driver, and 5 V
devices are used for the PMU and pixel readout. Fig. 15(b) shows the power
breakdown for the chip where the laser driver dominates the power consumption.

This section presents system-level measurement results for the US wireless
link, laser driver, and imaging frontend.

Figure 15: (a) Chip micrograph. (b) Breakdown of system power consumption.

###  IV-A Measurement Setup

Fig. 16 shows the measurement setup for demonstrating fully wireless operation
of the chip. In the acoustic setup, the piezo is submerged at 5 cm depth in a
tank of canola oil. An external focused transducer (V314-SU-F1.90IN-PTF,
Evident Scientific) at the surface of the tank transmits US signals to the
piezo. To minimize interference from US reflections on data uplink, an
acoustic absorber (Aptflex F28P, Precision Acoustics) is placed at the bottom
of the tank. An FPGA (Opal Kelly, XEM7010) generates the desired US pulse
sequence (as in Fig. 11) to control the chip. The timing of the pulse sequence
is programmed through a custom user interface that interfaces with the FPGA.
The generated waveforms are sent to a high-voltage transducer pulser board
(Max14808, Maxim Integrated) to drive the external transducer accordingly.

Figure 16: Measurement setup for wireless imaging.

The chip is directly connected with wires to the piezo for wireless power
harvesting and data transfer via US. It is located inside a black box to
reduce the background signal from ambient light during imaging. Slide-mounted
samples are placed directly on top of the chip. The chip drives the µLDs,
mounted on separate PCBs, to transilluminate the sample from above.
Admittedly, in vivo, the sample must be epi-illuminated between the sensor and
the tissue. Epi-illumination can be accomplished in the future by directing
the laser light through a glass separator or light guide plate placed on top
of the sensor [63, 64].

After taking an image, the backscattered US pulses are received by the
external transducer and captured on an oscilloscope for processing and
demodulation. To remove the pixel-to-pixel DC offsets due to the photodiode
dark current and mismatch in the readout circuitry, a dark image with the same
integration time but with the laser off is subtracted from the final
fluorescence image. The dark image is averaged to minimize its noise
contribution.

###  IV-B Ultrasound Wireless Power Transfer

Fig. 17(a) shows the measured PMU waveforms (VPIEZO+, VRECT, VCP, VCSTORE),
verifying wireless operation of the full system at 5 cm depth. In this
measurement, the system operates with an US power density of 221 mW/cm2, which
falls within 31% of FDA safety limits. Under this minimum required acoustic
power condition, VCP charges to 5.5 V in 50 s for the initial image. The
charging time decreases to 35 s for consecutive frames with a nonzero initial
VCP. The Charge-Up time can be further reduced by increasing US power
intensity, operating closer to the FDA limits. The output voltages of the
rectifier (VRECT) and charge pump ((VCP)) across different input voltages
(VPIEZO+) show a minimum VPIEZO+=2.42 V is required for stable operation of
the chip (Fig. S4).

Figure 17: Measured PMU waveforms during (a) Charge-Up and (b) Imaging and
Readout. (c) Measured backscatter waveforms.

Measured PMU waveforms during the Imaging and Readout states are presented in
Fig. 17(b). During Imaging (TEXP=8 ms), VCSTORE drops from 5.5–2.5 V while
supplying the laser with ILD=37.5 mA from the energy stored in CSTORE. VCP
remains at 5.5 V throughout Imaging and drops to 3.5 V during Readout.

Fig. 11(c) shows the measured waveforms while transmitting a single pixel data
packet via US backscattering. VPIEZO+ is modulated according to the serial
output of the memory ($\Theta$MOD) and the backscattered pulses are received
by the external transducer (VBACKSCATTER in Fig. 11(c)). The one bits
correspond to a smaller load impedance, but appear larger in amplitude than
the zero bits because the piezo is operated between series and parallel
resonance frequencies for maximum voltage harvesting.

Fig. 18(a) shows the total acoustic power and acoustic power density (ISPTA)
incident on the piezo surface area at 5 cm depth for transverse offsets along
the X or Y axis. Fig. 18(b) shows a similar measurement as the depth is
adjusted along the Z axis. The acoustic power density is measured with a
hydrophone (HGL-1000, Onda) and it is integrated over the piezo area to
measure the available acoustic power at the piezo surface. The reported
spatial-peak time-average intensity (ISPTA) of the acoustic field is the
relevant parameter in calculating FDA safety limits for diagnostic US [65].
For both transverse and depth offsets, the power decreases as the piezo moves
away from the focal point (near 5 cm depth) of the external transducer. The
measured transverse and axial FWHMs for ISPTA are 4.5 mm and 60 mm,
respectively. In the future, misalignment loss can be reduced through dynamic
focusing of the US with beam forming [66]. It should be noted that angular
misalignment of the piezo with respect to the US beam will also reduce the
harvested power [67, 68].

Figure 18: Harvested acoustic power vs. (a) transverse offset and (b) depth.

While charging VCP from 0–5.5 V, the overall electrical energy efficiency of
the PMU is 12.7%. The efficiency of the system in converting the available
acoustic energy on the face of the piezo to the electrical output energy of
the PMU is 3.3%. The output energy of the PMU is calculated by measuring the
energy stored in the CSTORE and CVCP and the total energy consumption of the
ASIC during Charge-Up. The input acoustic energy is calculated by integrating
the measured acoustic power density at the surface of the piezo (Fig. 18(a))
throughout this same period.

###  IV-C Ultrasound Data Uplink

At 5 cm depth, transmission of one image (11.52 kb) takes 890 ms, resulting in
a data rate of 13 kbps. The received backscattered waveform is processed and
demodulated to reconstruct the image as follows. First, the signal is
bandpass-filtered at the carrier frequency, windowed to select the bit
intervals, and then reconstructed with sinc interpolation. The peak-to-peak
amplitude is then measured for each pulse and compared with a predetermined
threshold to predict the bit value. The serial output of the chip serves as
the ground truth.

Fig. 19 shows a histogram of the backscattered signal amplitude for each bit
normalized to the threshold amplitude, demonstrating a clear separation
between one and zero bits. The measurement shows robust error-free
transmission of 90 frames, including a combination of dark frames and images
taken with the 650 nm and 455 nm lasers. The bimodal nature of the histogram
results from combining data across different imaging conditions and differing
interference from the high voltage pulsing of the external transducer on the
two pulses received within each interval of 2 ToFs. The device achieves a BER
better than 10-6 (0 out of 1,036,800 bits) with an average modulation index of
5.6%.

Figure 19: Measured bit error rate (BER) at 5cm depth in oil.

###  IV-D Laser Driver

Fig. 20 shows measurements of the laser driver and PTAT. The output current of
the laser driver (ILD) is measured with a precision measurement unit (B2912A,
Keysight). Fig. 20(a) shows the measured ILD across all DAC codes and Fig.
20(b) shows the percent change in ILD as the output voltage of the laser
driver, VLD−, drops from 3.5–0.4 V. This range corresponds to the VLD− for a
5.5–3.5 V drop on VCP accounting for the 2 V forward bias voltage of the 650
nm µLD. For DAC=5 (ILD=20 mA), there is less than 1% variation across the 3.1
V drop, corresponding to 1.3% variation in optical power output of the 650 nm
µLD.

Fig. 20(c) shows the variation in the 0.5 V PTAT reference across VCP measured
through the VADC0.5V LDO. As VCP drops from 5.5–3.5 V, the PTAT reference
varies around 2.5%, which has minimal effect on the ADC during Readout.

These results are an improvement over [32] where the reference current varied
11.5% over a 1.5 V drop, resulting in a 50% reduction in the laser output
power.

Figure 20: Measurements of (a) laser driver current (ILD) vs. DAC code, (b)
percent change in ILD vs. driver output voltage (VLD-), and (c) PTAT voltage
reference (VREF 0.5 V) variation vs. VCP.

###  IV-E Imaging Frontend

The photodiode responsivity is determined by measuring pixel output voltage
across a range of incident optical powers as shown in Fig. 21(a). We use a LED
with a collimator and beam expander to ensure uniform illumination of the
sensor. A narrow bandpass interference filter placed in front of the LED
selects a specific wavelength. Measurements are made at 535 nm and 705 nm,
near the center of the optical frontend passbands. The optical power output of
the LED is characterized with a power meter (PM100D, ThorLabs). In Fig. 21(a),
the slope indicates pixel gain in mV/pW with TEXP=8 ms. The photodiode
responsivity is calculated by dividing pixel gain by the transimpedance gain
of the CTIA. The pixels have a mean responsivity of 0.13 A/W (quantum
efficiency (QE=30%) and 0.21 A/W (QE=37%), at 535 nm and 705 nm respectively.

Figure 21: (a) Pixel output voltage vs. incident optical power. (b) Histogram
of measured dark current across pixels. (c) Measured pixel noise under dark
condition for a single frame and after 8 averages.

A histogram of the measured dark current across pixels with a Gaussian fit is
shown in Fig. 21(b). The mean dark current is 14.9 fA (7.7 aA/µm2) with a
standard deviation of 0.7 fA (0.4 aA/µm2). Fig. 21(c) shows the measured pixel
output noise in dark condition for different exposure times for a single frame
and an average of 8 frames. For TEXP=8 ms, the measured pixel output noise is
5.34 mVrms for a single frame and 1.87 mVrms after 8 averages. The output
noise increases with the exposure time due to the shot noise from the
increased dark signal.

The resolution of the imager is measured with a negative USAF target (Fig.
22(a)) overlaying a uniform layer of Cy5 NHS ester ($\lambda$EX=649 nm,
$\lambda$EM=670 nm) dissolved in PBS at 10 µM concentration. The dye is
contained with a 150 µm-thick glass coverslip and the target is placed on the
imager. The resolution measurements were conducted with wired power and data
transfer and using a fiber-coupled 650 nm laser for uniform illumination. Fig.
22(b) shows the sensor image of the element with 125 µm line spacing compared
to the microscope reference image in Fig. 22(c). The sensor images this
element at 50% contrast as calculated with the line scan in Fig. 22(d).
Contrast is calculated as $(V_{MAX}-V_{MIN})/(V_{MAX}+V_{MIN}-V_{BK})$, where
$V_{MAX}$ and $V_{MIN}$ are the maximum and minimum pixel values in the bright
and dark bars, respectively, and $V_{BK}$ is the background signal. Fig. 22(e)
shows the full contrast transfer function measured by imaging elements on the
target with line spacing ranging from 79–455 µm and calculating the contrast
for each. These results demonstrate that with the FOP, the imager can
distinguish line spacing as small as 100 µm with greater than 20% contrast.

Figure 22: Resolution measurements using (a) USAF target. Image of element
with 125 µm line width with the sensor (b) and a microscope (c). (d) Line scan
of image in (a). (e) Measured contrast transfer function.

To demonstrate three-color imaging, we image a sample containing 15µm-diameter
green ($\lambda$EX=505 nm, $\lambda$EM=515 nm, F8844, Thermo Fisher
Scientific), red ($\lambda$EX=645 nm, $\lambda$EM=680 nm, F8843, Thermo Fisher
Scientific), and NIR ($\lambda$EX=780 nm, $\lambda$EM=820 nm, DNQ-L069, CD
Bioparticles) fluorescent beads. The beads are suspended in 1× PBS solution at
a concentration of approximately 10 beads/µL. 50 µL of solution is pipetted
into a micro-well chamber slide for imaging. Imaging results are shown in Fig.
23. The sensor images are obtained wirelessly with ILD=18.5 mA, TEXP,GREEN=8
ms, TEXP,RED=16 ms, TEXP,NIR=8 ms. For each color channel, 4 frames are
averaged and the channels are colored and overlaid to make the multicolor
image. The sensor images show good correspondence with the reference image
taken with a bench-top fluorescence microscope (Leica DM-IRB). A few beads do
not appear in the sensor image due to non-uniform illumination from the µLDs.
There is also a line artifact visible in the NIR channel due to reflections
off the wire-bonds and that be mitigated through more careful fabrication as
detailed in [34].

Figure 23: 3-color imaging of fluorescent beads.

##  V Ex Vivo Imaging of Immune Response

We conducted an ex vivo mouse experiment to demonstrate the application of our
sensor to assessing the response to cancer immunotherapy through dual-color
fluorescence imaging of both effector and suppressor cells in the tumor
microenvironment. In this study, we measure response to immune checkpoint
inhibitors (ICIs), a class of immunotherapy that activates the immune system
against cancer by blocking interactions between effector and inhibitory immune
cells and cancer [69, 70]. A successful immune response to ICIs requires the
activation and proliferation of CD8+ T-cells, the most powerful effector cells
in the anticancer response, into the tumor microenvironment [71]. Therefore,
CD8+ T-cell infiltration has been identified as an indicator of a favorable
immune response [72]. However, CD8+ T-cell activation can be inhibited by
suppressor immune cells such as neutrophils, which regulate the immune system
and inflammation in the body and are associated with resistance to ICI
immunotherapy [73, 74]. Dual-color fluorescence imaging enables a differential
measurement of these two control mechanisms of the immune response with the
same imaging frontend which is not possible with clinical imaging modalities
such as MRI, PET, or CT.

###  V-A Experimental Design

Fig. S5 outlines the ex vivo experiment design, which uses two engineered
cancer models from [75], an LLC lung cancer model (engineered to resist ICIs)
and a B16F10 melanoma model (engineered to respond to ICIs). Both tumor models
show increased CD8+ T-cell infiltration over the course of treatment. However,
while the B16F10 tumors reliably respond, the LCC tumors are resistant to ICI
therapy. This resistance has been linked to a T-cell-driven inflammatory
response that triggers an influx of neutrophils into the tumor, suppressing
T-cell activation [75].

The experiment includes two groups of mice each bearing one type of tumor.
Each group consists of a mouse treated with a combination of PD-1 and CTLA-4
inhibitors, a class of ICIs [69], and an untreated mouse injected with a non-
therapeutic antibody for control. Three weeks after tumor implantation, the
tumors are harvested, sectioned to 4 µm-thick samples, and mounted on glass
slides. Two adjacent sections from each tumor are labeled separately with
fluorescent probes targeting CD8+ T-cells and neutrophils. CD8+ T-cells are
stained with a CD8+ antibody labeled with Cy5 ($\lambda$EX=649 nm,
$\lambda$EM=670 nm) and neutrophils are stained with a CD11b antibody labeled
with FAM ($\lambda$EX=492 nm, $\lambda$EM=518 nm).

Figure 24: Ex vivo imaging of mouse tumors with and without immunotherapy.
Imaging results for (a) the resistant tumor model (LLC) and (b) the responsive
model (B16F10). (c) Metrics for quantification of cell populations. (d)
Quantified results.

###  V-B Imaging Results

Images of the tumor samples are captured wirelessly with the sensor and
compared with reference images from a bench-top fluorescence microscope. Figs.
24(a) and (b) show the imaging results from the LLC (resistant) and B16F10
(responsive) groups, respectively. For each fluorescent channel, 8 frames are
acquired with the chip, using imaging parameters of ILD=18.5 mA, TEXP,Cy5=16
ms, and TEXP,FAM=8 ms. The sensor images are averaged across all frames. The
microscope images are overlaid with the cell nuclei of the entire sample,
stained with DAPI (blue in the image) to highlight the tumor area. The white
lines within the images indicate the boundaries of the tumor tissue. The
sensor images are qualitatively consistent with the microscope references,
albeit at a lower resolution and with varying intensity across the image due
to non-uniform illumination from the µLDs.

To quantify the results for each tumor model, the percent change in the
density of both cell types between the untreated and treated mice is
calculated according to the metrics in Fig. 24(c). Ground truth cell densities
are determined using the microscope images by counting the fraction of cell
nuclei (DAPI) labeled with the targeted probe (red and green channel). As the
sensor does not have single-cell resolution, the cell density in the sensor
images is determined by the fluorescence intensity in the tumor normalized by
the area bounded by the dashed white lines in Fig. 24(a) and (b). The
background signal is mostly canceled out by measuring percent change.

The quantified results from the sensor and microscope are shown in Fig. 24(d).
The sensor captures the general trends observed with the microscope,
corresponding with the results in [75]. The increase in the density of CD8+
T-cells in both B16F10 samples (sensor: 847%, microscope: 582%) and the LLC
samples (sensor: 38%, microscope: 191%) suggests an effector response to
immunotherapy in both models. However, a larger increase in CD11b density
after treatment in the LLC tumors (sensor: 66%, microscope: 75%) over the
B16F10 tumors (sensor: 42%, microscope: 51%), suggests resistance in the LLC
model due an increase in neutrophils. These trends would better reflect the
results in [75] with a larger sample size to account for heterogeneity across
the mice and neutrophil-specific biomarkers (CD11b also stains other myeloid
cells).

However, these results highlight the utility of multicolor fluorescence
imaging in evaluating the response to cancer immunotherapy, enabling a
differential measurement of both effector (e.g. CD8+ T-cell) and suppressor
(e.g. neutrophil) populations. As shown by the increase in CD8+ T-cells in
resistant LLC tumors, an increase in effector populations does not always
correlate with response as the effector cells may be inhibited by suppressor
cells. Therefore, simultaneously imaging suppressor populations such as
neutrophils has two advantages: (1) enabling a more accurate assessment of
response and (2) revealing the mechanisms of resistance (e.g. neutrophil
interference with CD8+ T-cells) that can be targeted with second-line
therapies (e.g. blocking T-cell-induced immunosuppressive inflammation
signaling as done in [75]). Future in vivo studies can highlight the unique
capability of our sensor to analyze real-time dynamics in the spatial
interactions of these populations, which is critical for developing a more
nuanced understanding of the immune response [18].

TABLE I: COMPARISON OF STATE-OF-THE-ART CHIP-SCALE FLUORESCENCE IMAGE SENSORS

##  VI Conclusion

We present a fully wireless implantable image sensor capable of multicolor
fluorescence imaging for real-time monitoring of response to cancer
immunotherapy. A comparison of our work with recent chip-scale fluorescence
imagers and sensors is shown in Table I. To the knowledge of the authors, our
work is the first to demonstrate fully wireless operation of the entire system
with biologically relevant samples. In [23], a battery is used for power. In
[32] the US link operates above FDA limits and low imager sensitivity limits
wireless imaging to high concentrations of fluorescent dye. With a power
harvesting frontend incorporating a cross-coupled charge-pump, we demonstrate
safe operation at 5 cm depth in oil with US power densities at 31% of FDA
limits. The robust communication link demonstrates a BER better than 10-6 with
a 13 kbps data rate. Moreover, optimization of the storage capacitor sizing
enables a small form factor of 0.09 cm3 demonstrated with a mechanical
assembly of the implant.

Our system is specifically designed for multicolor fluorescence imaging with a
three-channel laser driver to drive different color µLDs, an US downlink for
programming imaging and laser settings, and an optical frontend design
consisting of a multi-bandpass interference filter and a FOP. Our optical
frontend provides greater than 6 OD of excitation rejection of lasers within
15 nm of the filter band edge, a significant improvement over the CMOS metal
filters reported in [22, 23] and competitive performance with the combination
of absorption and interference filters in [24, 25]. To the best of our
knowledge, this work is the first chip-scale fluorescence imager capable of
three-color imaging, which we demonstrate through imaging fluorescent beads.
The pixel noise is on the same order of magnitude as [22, 23] despite these
works using pixel sizes accommodating large low-noise readout circuits with
higher power consumption.

By imaging CD8+ T-cells and neutrophils populations in ex vivo mouse tumors
with or without immunotherapy, we show how multicolor fluorescence imaging can
enable accurate identification of non-responders and their underlying
resistance mechanisms. Such sub-millimeter imaging of multiple biomarkers is
inaccessible to clinical imagers such as MRI, CT or PET and can inform
personalized treatment regimens addressing the wide variability in response to
immunotherapy across patients. With future work in biocompatible packaging and
integration of optics for epi-illumination, our platform can open the door to
real-time, chronic monitoring of the spatial interactions of multiple cell
populations deep in the body.

## Acknowledgments

The authors would like to thank sponsors of BSAC (Berkeley Sensors and
Actuators Center) and TSMC for chip fabrication. We appreciate technical
discussion and advice from Prof. Rikky Muller, Efthymios Papageorgiou, Hossein
Najafiaghdamand, and Mohammad Meraj Ghanbari. Thank you to Eric Yang, Jade
Pinkenburg, and Kingshuk Daschowdhury for their technical assistance. Finally,
we acknowledge Dr. Mohammad Naser from Biological Imaging Development CoLab
(BIDC) and Kristine Wong from Laboratory for Cell Analysis (LCA) for the
development of immunohistochemistry workflow and imaging.

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|  Micah Roschelle (Graduate Student Member, IEEE) received his B.S. degree in
electrical engineering from Columbia University, New York, NY, USA, in 2020.
He is currently pursuing a Ph.D. in electrical engineering and computer
sciences at the University of California, Berkeley, Berkeley, CA, USA. His
research interests include implantable medical devices, lensless fluorescence
imaging, and biomedical sensor design.
---|---
|  Rozhan Rabbani (Graduate Student Member, IEEE) received the B.Sc. degree
from Sharif University of Technology, Tehran, Iran, in 2018. She received her
Ph.D. degree from the Department of Electrical and Computer Sciences,
University of California Berkeley, Berkeley, CA, USA in 2024. At Sharif
University of Technology, she worked on analog and mixed-signal circuit design
to optimize power consumption for a wearable ECG sensor. She worked at Apple
Inc. during Summers 2020 and 2022 working on calibration and test automation
for high-speed applications. Her research at UC Berkeley was focused on
developing biomedical circuits and sensors, specifically implantable image
sensors for cancer therapy. She was the recipient of the Apple Ph.D.
Fellowship in Integrated Circuits in 2022, the 2024 SSCS Rising Stars, and the
2024 SSCS Predoctoral Achievement Award.
---|---
|  Surin Gweon (Graduate Student Member, IEEE) received the B.S. degree in
electrical engineering from Korea University, Seoul, South Korea, in 2018, and
the M.S. degree in electrical engineering from the Korea Advanced Institute of
Science and Technology (KAIST), Daejeon, South Korea, in 2020. She worked with
System LSI Business, Samsung Electronics Company Ltd., Hwaseong, South Korea
until 2023. She is currently pursuing the Ph.D. degree electrical engineering
and computer sciences at the University of California at Berkeley (UC
Berkeley), Berkeley, CA, USA. Her research interests include image sensor
front-end and mixed-mode computing for implantable biomedical applications.
---|---
|  Rohan Kumar (Graduate Student Member, IEEE) received his B.S. degree in
electrical engineering and computer science (EECS) from the University of
California, Berkeley, Berkeley, CA, USA, in 2024. He is currently pursuing a
Ph.D. in EECS at UC Berkeley. His interests include electronic design
automation, die-to-die interconnects, and open-source hardware.
---|---
|  Alec Vercruysse (Graduate Student Member, IEEE) received a B.S. degree in
engineering from Harvey Mudd College in Claremont, CA, USA in 2023. He is
currently pursuing a Ph.D. in electrical engineering and computer sciences at
the University of California, Berkeley. His interests include the system-level
design of circuits for implantable medical devices.
---|---
|  Nam Woo Cho Dr. Nam Woo Cho, MD, PhD is a physician scientist in radiation
oncology. Dr. Cho received his undergraduate degree from Harvard College, and
MD/PhD degrees from the University of Pennsylvania. He completed internship in
Internal Medicine at St. Mary’s Medical Center in San Francisco, and his
residency in radiation oncology at UCSF. Following his postdoctoral work with
Dr. Matthew Spitzer, he started his own research laboratory as an Assistant
Professor in the Department of Radiation Oncology and Department of
Otolaryngology-Head and Neck Surgery. His research focuses on understanding
fundamental immunologic mechanisms that govern responses to immune stimulating
therapies including radiation therapy and immune checkpoint inhibitors. Dr.
Cho leverages molecular, cellular, organismal, and computational platforms to
define novel mechanisms, pioneering the next generation of radio- and immune-
therapeutics.
---|---
|  Matthew H. Spitzer received the B.S. degree from Georgetown University,
Washington, DC, USA and the Ph.D. degree from Stanford University, Stanford,
CA, USA, in 2015. In 2016, he joined University of California San Francisco
(UCSF), San Francisco, CA, USA as a UCSF Parker Fellow and a Sandler Faculty
Fellow. He is currently Associate Professor in the Departments of
Otolaryngology-Head and Neck Surgery and Microbiology & Immunology at UCSF and
an investigator of the Parker Institute for Cancer Immunotherapy, San
Francisco, USA. His research aims to develop understanding of how the immune
system coordinates its responses across the organism with an emphasis on tumor
immunology by combining methods in experimental immunology and cancer biology
with computation.
---|---
|  Ali M. Niknejad (Fellow, IEEE) received the B.S.E.E. degree from the
University of California at Los Angeles, Los Angeles, CA, USA, in 1994, and
the master’s and Ph.D. degrees in electrical engineering from the University
of California at Berkeley (UC Berkeley), Berkeley, CA, in 1997 and 2000,
respectively. He is currently a Professor with the EECS Department, UC
Berkeley, the Faculty Director of the Berkeley Wireless Research Center
(BWRC), Berkeley, and the Associate Director of the Center for Ubiquitous
Connectivity. His research interests include wireless and broadband
communications and biomedical imaging and sensors, integrated circuit
technology (analog, RF, mixed signal, and mm-wave), device physics and compact
modeling, and applied electromagnetics. Prof. Niknejad and his coauthors
received the 2017 IEEE Transactions on Circuits and Systems—I: Regular Papers
Darlington Best Paper Award, the 2017 Most Frequently Cited Paper Award
(2010–2016) at the Symposium on VLSI Circuits, and the CICC 2015 Best Invited
Paper Award. He was a recipient of the 2012 ASEE Frederick Emmons Terman Award
for his textbook on electromagnetics and RF integrated circuits. He was a co-
recipient of the 2013 Jack Kilby Award for Outstanding Student Paper for his
work on an efficient Quadrature Digital Spatial Modulator at 60 GHz, the 2010
Jack Kilby Award for Outstanding Student Paper for his work on a 90-GHz pulser
with 30 GHz of bandwidth for medical imaging, and the Outstanding Technology
Directions Paper at ISSCC 2004 for co-developing a modeling approach for
devices up to 65 GHz.
---|---
|  Vladimir M. Stojanović (Fellow, IEEE) received the Dipl. Ing. degree from
the University of Belgrade, Belgrade, Serbia, in 1998, and the Ph.D. degree in
electrical engineering from Stanford University, Stanford, CA, USA, in 2005.
He was with Rambus, Inc., Los Altos, CA, USA, from 2001 to 2004; and the
Massachusetts Institute of Technology, Cambridge, MA, USA, as an Associate
Professor, from 2005 to 2013. He is currently a Professor of electrical
engineering and computer sciences with the University of California at
Berkeley, Berkeley, CA, USA, where he is also a Faculty CoDirector of the
Berkeley Wireless Research Center (BWRC). His current research interests
include the design, modeling, and optimization of integrated systems, from
CMOS-based VLSI blocks and interfaces to system design with emerging devices,
such as NEM relays and silicon photonics, design and implementation of energy-
efficient electrical and optical networks, and digital communication
techniques in high-speed interfaces and high-speed mixed-signal integrated
circuit (IC) design. Dr. Stojanović was a recipient of the 2006 IBM Faculty
Partnership Award, the 2009 NSF CAREER Award, the 2008 ICCAD William J.
McCalla, the 2008 IEEE TRANSACTIONS ON ADVANCED PACKAGING, and the 2010 ISSCC
Jack Raper Best Paper and 2020 ISSCC Best Forum Presenter Awards. He was a
Distinguished Lecturer of IEEE Solid-State Circuits Society from 2012 to 2013.
---|---
|  Mekhail Anwar (Member, IEEE) received the B.A. degree in physics from the
University of California Berkeley (UC Berkeley), Berkeley, CA, USA, where he
graduated as the University Medalist, the Ph.D. degree in electrical
engineering and computer sciences from the Massachusetts Institute of
Technology, Cambridge, MA, USA, in 2007, and the M.D. degree from the
University of California San Francisco (UCSF), San Francisco, CA, in 2009. In
2014, he completed a Radiation Oncology residency with UCSF. In 2014, he
joined the faculty with the Department of Radiation Oncology, UCSF, with a
joint appointment in Electrical Engineering and Computer Sciences at UC
Berkeley (in 2021), where he is currently an Associate Professor. His research
focuses on developing sensors to guide cancer care using integrated-circuit
based platforms. His research centers on directing precision cancer therapy
using integrated circuit-based platforms to guide therapy. His work in chip
scale imaging has been recognized with awards from the DOD (Physician Research
Award) and the NIH (Trailblazer), and in 2020 he was awarded the prestigious
DP2 New Innovator Award for work on implantable imagers. At UCB and UCSF he
focuses on the development of implantable sensors across both imaging,
molecular sensing and radiation therapy. He is board certified in Radiation
Oncology and maintains a clinical practice specializing in the treatment of GI
malignancies with precision radiotherapy.
---|---

Figure 25: (a) LDO schematic. (b–d) different error amplifier topologies.
Table SI details design parameters for each of the 5 different LDOs. Figure
26: Power-on reset (POR) circuit schematic. Initially, when the chip is
charging up and VCP is below 3.9 V, the diodes, D1–D5, do not conduct current
and the gate of M2 is pulled low. Therefore, M2 is off and the POR signal is
pulled low, continuously resetting the finite state machine (FSM). When VCP
reaches 3.9V, D1–D5 turn on and the POR signal will be pulled high by M2,
allowing the FSM to function normally. R1 and C1 prevent sudden fluctuations
on VCP from triggering changes in the POR signal. As VCP falls below 3.9 V
during the Readout state, the feedback transistor, M1, ensures that M2 stays
on even as D1–D4 turn off, maintaining a high POR signal as long as the
digital 1.8 V LDO is still operational. The 3.9 V POR voltage is selected to
be slightly higher than the 3.5 V minimum operational voltage on VCP, to
ensure stable operation of the chip as the FSM wakes up. Figure 27: ADC with
conventional differential charge-redistribution SAR architecture. The ADC
compares the pixel output voltage (VS) with the CDS reset voltage (VR) and
produces an 8-bit digital pixel value. In the pixel architecture shown in Fig.
14(a), the output of the in-pixel CTIA is initially set to 0.6 V during the
reset phase (TRST). The CTIA is supplied by the 1.8 V analog LDO such that its
maximum output voltage is 1.6 V, resulting in a dynamic range of 1 V. During
Readout, the CDS outputs—pixel signal voltage (VS) and reset voltage (VR)—are
level-shifted up 1 V by the in-pixel source followers before the ADC samples
them. Therefore, the common-mode input of the comparator is set to 2.1 V,
which is provided by the VADC2.1V LDO. This voltage is selected to be halfway
between the minimum signal at the ADC input, corresponding to the CDS reset
voltage (VR=1.6 V), and the maximum achievable pixel signal considering the
CTIA dynamic range (VS=1.6+1 V=2.6 V).

Despite the 1 V headroom for the pixel output signal and given the typically
low photocurrent signals from fluorescence imaging, the ADC dynamic range is
set to 0.5 V, which dominates over the dynamic range of the CTIA. The ADC
dynamic range is set through the VADC0.5V LDO and is selected to achieve an
LSB of 1.95 V, which adds negligent quantization noise to pixel readout noise
(see Fig. 21(c) in the main text). This dynamic range is sufficient for
capturing signals in the maximum exposure setting of 248 ms, where dark
current uses up 338 mV of the dynamic range.

The strong-arm comparator utilizes PMOS input devices and operates on the 3.3
V supply with digital logic operating in the 1.8 V domain. The capacitive DAC
(C1–C10) is implemented with MIM capacitors where the smallest capacitor, C1
(40.56 fF), consists of two minimum-size unit capacitors of 20.28 fF each.

While the ADC has a 9-bit output, the 9th sign bit is discarded and is not
stored in the memory or transmitted via US backscatter. This sign bit is
unnecessary because VS is always greater than VR even when no light is
incident on the pixel: given the average pixel dark current of 14.9 fA and the
11 fF in-pixel integration cap, even at the shortest exposure time, TEXP=8 ms,
VS is expected to be 10.8 mV (~5.5 ADC LSBs) greater than VR on average. One
conversion cycle of the ADC lasts 15 CLK cycles which is 16.3 µs assuming a
920 kHz CLK from the US carrier. The simulated effective number of bits (ENOB)
of the extracted ADC with the buffers, LDOs and PTAT is 7.93 bits. Figure 28:
Measured harvested voltage at the output of the rectifier (VRECT) and the
output of the charge pump (VCP) for different voltages at the input of the
rectifier (VPIEZO+). A minimum voltage of VPIEZO+=2.42 V is required to
harvest VCP=3.9 V, high enough to trigger the POR (Fig. S2) and, thus, ensure
stable operation of the ASIC. This voltage is 27% lower than for nominal
operation of the chip (VCP=5.5 V), which requires VPIEZO+=3.3 V. VRECT is less
than VPIEZO+ due to the nonzero |VDS| of the actively controlled PMOS switches
(M3 and M4 in Fig. 9(a)) in the active rectifier. Figure 29: Experimental
design for the ex vivo mouse experiment.