*Article* **A Comparative Analysis of Printed Circuit Boards with Surface-Mounted and Embedded Components under Natural and Forced Convection**

**Maksim Korobkov 1 , Fedor Vasilyev 1,\* and Vladimir Mozharov 2**

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** This article is dedicated to the research of the physical reliability of electronic devices. It consists of a comparative thermal analysis of the cooling efficiency of a surface-mounted and an embedded component on a printed circuit board. A simulated finite element model of heat distribution over a printed circuit board with a surface component was constructed. An experiment confirmed the objectivity of the modeling results. The component's temperature was then analyzed depending on the installation method (surface and embedded) and the cooling method (natural and forced with varying airflow velocities). The results showed that the temperature of the embedded component was less than the temperature of the surface-mounted component under natural convection and, in most cases, under forced convection (with an airflow velocity of forced cooling under 16 m s ).

**Keywords:** PCB reliability; embedded components; finite element analysis; thermal analysis

## **1. Introduction**

The embedding of electronic components into the base of a printed circuit board (PCB) is a promising direction for developing modern electronic system components for applications such as measuring, navigation [1–3], and electric power systems [4,5]. Furthermore, this technology potentially reduces the size of manufactured electronic devices, increases the density of interconnections, allows the application of new design solutions, improves device performance by placing components closer together and reduces parasitic inductances and capacitances [6,7]. In addition, the use of embedded components should increase the manufacturability of devices because the production of such PCBs combines the manufacturing and assembly processes.

However, the issue of ensuring the reliability of both electronic devices, in general, [8–18] and PCBs with embedded components, in particular [19,20], is very relevant. It is especially necessary to research the processes that led to breakage (failure physics), and the development of design rules based on these processes to ensure the reliability of electronic devices (design for reliability) [21]. For example, it has already been experimentally proven that, under thermal cycling [22] and drop test conditions [23], PCBs with embedded components are more reliable than PCBs with surface-mounted components.

Below (Figure 1) is the distribution between the physical factors that most often cause failures in electronic modules [24–26]. Temperature fluctuations and vibrations are the main causes of deformation in PCBs and the connections on it. This leads to a breakdown in the electrical contact, detachment of the component, delamination of the base material and the appearance of cracks in it, as well as the destruction of metalized holes. Humidity and dust cause contamination, leading to the destruction of conductors and elements. In addition, humidity induces electrochemical migration processes: it causes short circuits between conductors and leads to the formation of conductive anodic filaments (CAF).

**Citation:** Korobkov, M.; Vasilyev, F.; Mozharov, V. A Comparative Analysis of Printed Circuit Boards with Surface-Mounted and Embedded Components under Natural and Forced Convection. *Micromachines* **2022**, *13*, 634. https:// doi.org/10.3390/mi13040634

Academic Editor: Francisco Perdigones

Received: 28 February 2022 Accepted: 14 April 2022 Published: 17 April 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** Distribution of the most frequent causes of electronic failures [22]. **Figure 1.** Distribution of the most frequent causes of electronic failures [22].

More than half of failures are due to thermal influences due to the wide variety of materials that make up the electronic device. They have different characteristics that should agree with each other. Below are some reasons for thermal failures in electronic More than half of failures are due to thermal influences due to the wide variety of materials that make up the electronic device. They have different characteristics that should agree with each other. Below are some reasons for thermal failures in electronic modules:

addition, humidity induces electrochemical migration processes: it causes short circuits between conductors and leads to the formation of conductive anodic filaments (CAF).

as warping and twisting and its stratification. Solving these problems is necessary, especially for power electronics. In addition, the use of embedded components can be used to increase temperature stability and improve the cooling efficiency of electronics. Embedding components inside PCBs allow, on the one hand, the provision of better heat transfer directly from the component but, on the other hand, also reduce the heat transferred by air cooling. Based on the above, a comparative analysis of thermal processes in PCBs with embedded components and PCBs with a Solving these problems is necessary, especially for power electronics. In addition, the use of embedded components can be used to increase temperature stability and improve the cooling efficiency of electronics. Embedding components inside PCBs allow, on the one hand, the provision of better heat transfer directly from the component but, on the other hand, also reduce the heat transferred by air cooling. Based on the above, a comparative analysis of thermal processes in PCBs with embedded components and PCBs with a traditional surface mounting method is of interest.

#### traditional surface mounting method is of interest. **2. Materials and Methods**

**2. Materials and Methods**  The experimental study of thermal processes is associated with some difficulties, including significant costs associated with the production of samples, the definition of test methods and the variation of parameters between experimental samples. Simulation modeling based on finite element analysis (FEA) facilitates such a study. However, very often, The experimental study of thermal processes is associated with some difficulties, including significant costs associated with the production of samples, the definition of test methods and the variation of parameters between experimental samples. Simulation modeling based on finite element analysis (FEA) facilitates such a study. However, very often, incorrect models are used.

incorrect models are used. Therefore, for thermal analysis the following were required:

#### mounting method. *2.1. Experiment to Determine the Thermal Distribution on a PCB*

*2.1. Experiment to Determine the Thermal Distribution on a PCB*  The most straightforward heat source is a resistor attached to an FR-4 fiberglass work piece. By applying a voltage to the resistor terminals, we obtained a picture of the heat distribution on the work piece using a thermal imager.

A resistor was fixed with glue on a 1.5 mm thick FR-4 fiberglass billet in the 0805 case with 510 ± 1% Ohm resistance. When gluing, the maximum contact of the resistor with the fiberglass was ensured. Wires for connecting electric current were soldered to the side surfaces of the resistor. It should be noted that the wires soldered to the resistor were a strong heat sink. Thus, the diameter of the wires should be chosen as the minimum necessary. In our case, we wanted to ensure the heat dissipation of the resistor from 0.5 to 1 W, and we applied a voltage of 20 V to the resistor terminals. Then, the power allocated was 0.784 W, and the current was approximately 39 mA. Considering the short duration of exposure and the experimental nature of use, we used AWG 24 wires with a diameter of 0.51 mm to supply current. To further simplify the analysis of the heat distribution over the PCB, risks were applied to the board in 5 mm increments. The marks were needed to analyze the image obtained using a thermal imager. The created experimental model is shown in Figure 2. A resistor was fixed with glue on a 1.5 mm thick FR-4 fiberglass billet in the 0805 case with 510 ± 1% Ohm resistance. When gluing, the maximum contact of the resistor with the fiberglass was ensured. Wires for connecting electric current were soldered to the side surfaces of the resistor. It should be noted that the wires soldered to the resistor were a strong heat sink. Thus, the diameter of the wires should be chosen as the minimum necessary. In our case, we wanted to ensure the heat dissipation of the resistor from 0.5 to 1 W, and we applied a voltage of 20 V to the resistor terminals. Then, the power allocated was 0.784 W, and the current was approximately 39 mA. Considering the short duration of exposure and the experimental nature of use, we used AWG 24 wires with a diameter of 0.51 mm to supply current. To further simplify the analysis of the heat distribution over the PCB, risks were applied to the board in 5 mm increments. The marks were needed to analyze the image obtained using a thermal imager. The created experimental model is shown in Figure 2. A resistor was fixed with glue on a 1.5 mm thick FR-4 fiberglass billet in the 0805 case with 510 ± 1% Ohm resistance. When gluing, the maximum contact of the resistor with the fiberglass was ensured. Wires for connecting electric current were soldered to the side surfaces of the resistor. It should be noted that the wires soldered to the resistor were a strong heat sink. Thus, the diameter of the wires should be chosen as the minimum necessary. In our case, we wanted to ensure the heat dissipation of the resistor from 0.5 to 1 W, and we applied a voltage of 20 V to the resistor terminals. Then, the power allocated was 0.784 W, and the current was approximately 39 mA. Considering the short duration of exposure and the experimental nature of use, we used AWG 24 wires with a diameter of 0.51 mm to supply current. To further simplify the analysis of the heat distribution over the PCB, risks were applied to the board in 5 mm increments. The marks were needed to analyze the image obtained using a thermal imager. The created experimental model is shown in Figure 2.

The most straightforward heat source is a resistor attached to an FR-4 fiberglass work piece. By applying a voltage to the resistor terminals, we obtained a picture of the heat

The most straightforward heat source is a resistor attached to an FR-4 fiberglass work piece. By applying a voltage to the resistor terminals, we obtained a picture of the heat

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distribution on the work piece using a thermal imager.

distribution on the work piece using a thermal imager.

**Figure 2.** Experimental model. **Figure 2.** Experimental model.**Figure 2.** Experimental model.

The experiment involved heating the work piece in a vertical position. Below (Figure 3) is a picture of the heat distribution obtained during the experiment (the dissipated power was 0.784 W). The experiment involved heating the work piece in a vertical position. Below (Figure 3) is a picture of the heat distribution obtained during the experiment (the dissipated power was 0.784 W). The experiment involved heating the work piece in a vertical position. Below (Figure 3) is a picture of the heat distribution obtained during the experiment (the dissipated power was 0.784 W).

**Figure 3.** The picture of the heat distribution on the work piece during the experiment; the red contour highlights the heating area in which the temperature was above 303 K: (**a**) wires were laid over the work piece; (**b**) wires were laid under the work piece. **Figure 3.** The picture of the heat distribution on the work piece during the experiment; the red contour highlights the heating area in which the temperature was above 303 K: (**a**) wires were laid over the work piece; (**b**) wires were laid under the work piece. **Figure 3.** The picture of the heat distribution on the work piece during the experiment; the red contour highlights the heating area in which the temperature was above 303 K: (**a**) wires were laid over the work piece; (**b**) wires were laid under the work piece.

As can be seen, in the vertical position, a drop-shaped pattern was observed, which showed the fact of convection heating of the work piece over the component. According to the results of a series of measurements, the steady-state temperature was 405 ± 2 K. In addition, the arrangement of wires on the work piece did not affect the pattern of heat distribution: the results of the experiment with different arrangements of wires were almost identical (Figure 3). addition, the arrangement of wires on the work piece did not affect the pattern of heat distribution: the results of the experiment with different arrangements of wires were almost identical (Figure 3).

As can be seen, in the vertical position, a drop-shaped pattern was observed, which showed the fact of convection heating of the work piece over the component. According to the results of a series of measurements, the steady-state temperature was 405 ± 2 K. In

Based on the obtained picture of the distribution of heat and temperature of the component, a future assessment of the correctness of the constructed simulation model was possible. Based on the obtained picture of the distribution of heat and temperature of the component, a future assessment of the correctness of the constructed simulation model was possible.

#### *2.2. Development of a Simulation Model of Thermal Processes 2.2. Development of a Simulation Model of Thermal Processes*

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To objectively model conduction and convection processes of heat transfer, we needed to choose software that allowed computational fluid dynamics (CFD) problems to be solved. Therefore, to solve this problem, the "Fluid Flow" module of the ANSYS software package was selected using the Fluent solver. To objectively model conduction and convection processes of heat transfer, we needed to choose software that allowed computational fluid dynamics (CFD) problems to be solved. Therefore, to solve this problem, the "Fluid Flow" module of the ANSYS software package was selected using the Fluent solver.

Also, several additional conditions needed to be imposed on the simulation model, taking into account all the particular features of the model, which are called boundary conditions [27]. The boundary conditions included the geometric model of the system, the physical properties of the system and the materials it consists of and the parameters of the process. Also, several additional conditions needed to be imposed on the simulation model, taking into account all the particular features of the model, which are called boundary conditions [27]. The boundary conditions included the geometric model of the system, the physical properties of the system and the materials it consists of and the parameters of the process.

As a geometric model, a 3D model of the work piece was created, consisting of a 50 × 50 × 1.5 mm FR-4 fiberglass base, with a simplified model of a 2 × 1.2 × 0.5 mm resistor installed on it. Wires with a diameter of 0.51 mm and a length of 45 mm were connected to the resistor using solder. This length was because, in the experiment conducted at this length, the temperature that the wire was comparable to the ambient temperature. In addition, based on the experimental results, the work piece dimensions were reduced since this should not have affected the heat distribution pattern, but would increase the calculation speed. As a geometric model, a 3D model of the work piece was created, consisting of a 50 × 50 × 1.5 mm FR-4 fiberglass base, with a simplified model of a 2 × 1.2 × 0.5 mm resistor installed on it. Wires with a diameter of 0.51 mm and a length of 45 mm were connected to the resistor using solder. This length was because, in the experiment conducted at this length, the temperature that the wire was comparable to the ambient temperature. In addition, based on the experimental results, the work piece dimensions were reduced since this should not have affected the heat distribution pattern, but would increase the calculation speed.

To solve the CFD problems, it was necessary that the geometric model had a finite volume. To determine this volume, we placed the 3D model of the work piece in the center of the cylinder (Figure 4). The entire volume inside the cylinder, excluding the volume of the PCB, was filled with air. The cylinder was larger than the researched work piece to exclude the cylinder's influence on the simulation results: the outer diameter of the cylinder was 105 mm, the inner diameter was 100 mm, the height was 200 mm. Thus, a geometric model had been obtained, which needed to be divided into finite elements. To solve the CFD problems, it was necessary that the geometric model had a finite volume. To determine this volume, we placed the 3D model of the work piece in the center of the cylinder (Figure 4). The entire volume inside the cylinder, excluding the volume of the PCB, was filled with air. The cylinder was larger than the researched work piece to exclude the cylinder's influence on the simulation results: the outer diameter of the cylinder was 105 mm, the inner diameter was 100 mm, the height was 200 mm. Thus, a geometric model had been obtained, which needed to be divided into finite elements.

**Figure 4.** Cross-section of the final view of the geometric model: 1—component; 2—base; 3—solder; 4—wire; 5—cylinder.

In the model, the part of the airflow located next to the component and the base made of fiberglass was of the most significant interest since it carries out the convective heat transfer from the component. Therefore, the linear dimensions of the mesh in the component and base areas were reduced to 0.25 mm and 0.5 mm, respectively. As a result, the following grid was created on the geometric model (Figure 5). Below are the specified shape and the maximum allowable linear size of the mesh element for each model object (Table 1). In the model, the part of the airflow located next to the component and the base made of fiberglass was of the most significant interest since it carries out the convective heat transfer from the component. Therefore, the linear dimensions of the mesh in the component and base areas were reduced to 0.25 mm and 0.5 mm, respectively. As a result, the following grid was created on the geometric model (Figure 5). Below are the specified shape and the maximum allowable linear size of the mesh element for each model object (Table 1).

**Figure 4.** Cross-section of the final view of the geometric model: 1—component; 2—base; 3—solder;

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**Figure 5.** The cross-section of the grid created on the geometric model: 1—component; 2—base; 3 solder; 4—wire; 5—the air around the component, solder and wires; 6—the air around the base; 7 air. **Figure 5.** The cross-section of the grid created on the geometric model: 1—component; 2—base; 3—solder; 4—wire; 5—the air around the component, solder and wires; 6—the air around the base; 7—air.

4—wire; 5—cylinder.

The initial conditions of the process were also set:

**Figure 6.** Heat distribution on the surface of the fiberglass base in the simulation model. **Figure 6.** Heat distribution on the surface of the fiberglass base in the simulation model.

Thus, the created model with sufficient accuracy reflected the investigated heating processes, which allowed it to be used in further experiments. Next, the properties of solid materials were set, namely the following: density, specific heat capacity, thermal conductivity (Table 2).

Four models were created based on the previously obtained results to analyze the effect of the airflow velocity on the temperature of the surface and embedded component (Figure 7). It was also necessary to determine the air parameters (Table 3). Since the model did not assume the transition of air from a gaseous state to a liquid state, its behavior could be fully described by the model of an ideal gas.

this aspect in the model, the forced airflow was directed from top to bottom.

**Table 3.** Air properties.

 (**a**) (**b**) (**c**) (**d**) **Figure 7.** Geometric models of the experiment (side view): (**a**) surface component, without a copper layer; (**b**) embedded component, without a copper layer; (**c**) surface component, with a copper layer; (**d**) embedded component, with a copper layer. Further clarification of the conditions of the process were made: during the construction, a standard type of pressure solver was used, as well as an absolute velocity model, since it was assumed that the airflow to the area under study occurred without vortices. The k − ε model simulated the turbulence process [28]. The model was calculated using the SIMPLEC algorithm to calculate the arithmetic mean values of the scalar in adjacent cells separated by a face (green Gauss cell base).

#### *2.3. Analysis of the Constructed Simulation Model*

Usually, in real printed circuit boards, the components have a permanent conductive heat sink through the conductors. In future modeling, we will consider only boundary cases. In the first case (Figure 7a,b), the components were attached directly to the When comparing the obtained picture (Figure 6) with the result of the experiment (Figure 3), it could be seen that the results had a similar shape, with minor differences.

The component's temperature in the simulation model was 415.8 K, comparable to the temperature values obtained during the experiment (see Section 2.1). **Figure 6.** Heat distribution on the surface of the fiberglass base in the simulation model.

Thus, the created model with sufficient accuracy reflected the investigated heating processes, which allowed it to be used in further experiments. Thus, the created model with sufficient accuracy reflected the investigated heating processes, which allowed it to be used in further experiments.

#### **3. Results 3. Results**

The constructed simulation model gave us the opportunity to study the cooling processes of the PCB under conditions of natural and forced convection. In order to do this, we needed to set the direction of the forced airflow in the model. It could be assumed that the forced movement of air from top to bottom with small values of the airflow velocity would prevent the natural removal of heat from the PCB. However, how much this phenomenon affected the component's temperature was unknown. Therefore, to investigate this aspect in the model, the forced airflow was directed from top to bottom. The constructed simulation model gave us the opportunity to study the cooling processes of the PCB under conditions of natural and forced convection. In order to do this, we needed to set the direction of the forced airflow in the model. It could be assumed that the forced movement of air from top to bottom with small values of the airflow velocity would prevent the natural removal of heat from the PCB. However, how much this phenomenon affected the component's temperature was unknown. Therefore, to investigate this aspect in the model, the forced airflow was directed from top to bottom.

Four models were created based on the previously obtained results to analyze the effect of the airflow velocity on the temperature of the surface and embedded component (Figure 7). Four models were created based on the previously obtained results to analyze the effect of the airflow velocity on the temperature of the surface and embedded component (Figure 7).

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**Figure 7.** Geometric models of the experiment (side view): (**a**) surface component, without a copper layer; (**b**) embedded component, without a copper layer; (**c**) surface component, with a copper layer; (**d**) embedded component, with a copper layer. **Figure 7.** Geometric models of the experiment (side view): (**a**) surface component, without a copper layer; (**b**) embedded component, without a copper layer; (**c**) surface component, with a copper layer; (**d**) embedded component, with a copper layer.

Usually, in real printed circuit boards, the components have a permanent conductive heat sink through the conductors. In future modeling, we will consider only boundary cases. In the first case (Figure 7a,b), the components were attached directly to the Usually, in real printed circuit boards, the components have a permanent conductive heat sink through the conductors. In future modeling, we will consider only boundary cases. In the first case (Figure 7a,b), the components were attached directly to the fiberglass. In the second case (Figure 7c,d), the components were attached to a copper polygon without any conductive pattern.

Similar features of the models were a 50 × 50 × 1.5 mm FR-4 fiberglass base with an attached component 0805 was placed in the cylinder. The component, in turn, was a heat source with a volumetric power density equal to 0.654 W mm3 . Again, the initial conditions described above were used (Sections 2.1 and 2.2).

Based on the simulation results (Figure 8), graphs of the component temperature dependence on the input current velocity (Figure 9) were constructed for models without a copper layer and the following conclusions were made:

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source with a volumetric power density equal to 0.654

source with a volumetric power density equal to 0.654

copper layer and the following conclusions were made:

copper layer and the following conclusions were made:

described above were used (Sections 2.1 and 2.2).

described above were used (Sections 2.1 and 2.2).

polygon without any conductive pattern.

polygon without any conductive pattern.

In this case, the component temperatures were 11 K and 9 K higher than the natural cooling for the surface and embedded components. value could be considered the absolute value of air velocity due to natural convection. In this case, the component temperatures were 11 K and 9 K higher than the natural cooling for the surface and embedded components. value could be considered the absolute value of air velocity due to natural convection. In this case, the component temperatures were 11 K and 9 K higher than the

fiberglass. In the second case (Figure 7c,d), the components were attached to a copper

fiberglass. In the second case (Figure 7c,d), the components were attached to a copper

Similar features of the models were a 50 × 50 × 1.5 mm FR-4 fiberglass base with an attached component 0805 was placed in the cylinder. The component, in turn, was a heat

Similar features of the models were a 50 × 50 × 1.5 mm FR-4 fiberglass base with an attached component 0805 was placed in the cylinder. The component, in turn, was a heat

Based on the simulation results (Figure 8), graphs of the component temperature dependence on the input current velocity (Figure 9) were constructed for models without a

Based on the simulation results (Figure 8), graphs of the component temperature dependence on the input current velocity (Figure 9) were constructed for models without a

temperature of the embedded component was lower than the temperature of the surface component. This phenomenon was because the embedded component dissipated its power only into the base material, which increased the dispersion area and cooling efficiency. On the other hand, fiberglass has a low thermal conductivity, which did not allow effective cooling of the built-in component at high airflow rates. 2. The components' temperature increased at low airflow velocity values, confirming the assumption made earlier. However, it could be noticed that the temperature of

temperature of the embedded component was lower than the temperature of the surface component. This phenomenon was because the embedded component dissipated its power only into the base material, which increased the dispersion area and cooling efficiency. On the other hand, fiberglass has a low thermal conductivity, which did not allow effective cooling of the built-in component at high airflow rates. 2. The components' temperature increased at low airflow velocity values, confirming the assumption made earlier. However, it could be noticed that the temperature of

1. Under conditions of natural and forced convection (at airflow values up to 16 ୫

1. Under conditions of natural and forced convection (at airflow values up to 16 ୫

the components reached its maximum at a forced flow rate of 0.25 ୫

the components reached its maximum at a forced flow rate of 0.25 ୫

natural cooling for the surface and embedded components.

୫୫య. Again, the initial conditions

୫୫య. Again, the initial conditions

ୱ

ୱ

ୱ ), the

ୱ ), the

. This velocity

. This velocity

**Figure 8.** Heat distribution according to models without a copper layer under natural convection conditions: (**a**) surface component; (**b**) embedded component. **Figure 8.** Heat distribution according to models without a copper layer under natural convection conditions: (**a**) surface component; (**b**) embedded component. **Figure 8.** Heat distribution according to models without a copper layer under natural convection conditions: (**a**) surface component; (**b**) embedded component.

**Figure 9.** Dependence of the component temperature on the airflow rate in models without a copper layer.

From a practical point of view, the existing equipment cooling systems produced a flow rate of no more than 15 m s . However, in real conditions, the airflow velocity in electronic equipment does not exceed 3 m s . Therefore, the following conclusion could be drawn: in most use cases, namely in conditions of natural cooling and low-performance forced cooling, the built-in component will have a lower temperature compared to the surface one, which would positively affect its reliability and the reliability of the electronic device as a whole.

Let us consider models with a copper layer (Figure 10) and graphs of the component temperature dependence on the input flow rate (Figure 11). The introduction of a copper layer significantly reduced the temperature of the component and the temperature gradient on the PCB. When the flow velocity of the blown air was less than 16 m s , the temperature of the embedded component was lower than the temperature of the surface component, which was similar to models without a copper layer. Under natural cooling conditions, the difference between the components' temperatures was about 3 K, and in the presence of forced cooling was about 1.5 K. However, the number of components on the real PCBs was

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ୱ

layer.

flow rate of no more than 15 ୫

device as a whole.

tronic equipment does not exceed 3 ୫

tens and hundreds, and their power could be higher than in the considered model. In this case, the advantage of the embedded mounting would be more significant. the slight difference between the temperatures of the PCB and the environment. Supplementary Materials for the article include the results of simulation of heat distribution by models under conditions of forced convection at different airflow rates.

**Figure 9.** Dependence of the component temperature on the airflow rate in models without a copper

drawn: in most use cases, namely in conditions of natural cooling and low-performance forced cooling, the built-in component will have a lower temperature compared to the surface one, which would positively affect its reliability and the reliability of the electronic

ture of the embedded component was lower than the temperature of the surface component, which was similar to models without a copper layer. Under natural cooling conditions, the difference between the components' temperatures was about 3 K, and in the presence of forced cooling was about 1.5 K. However, the number of components on the real PCBs was tens and hundreds, and their power could be higher than in the considered model. In this case, the advantage of the embedded mounting would be more significant. Also, in models with a copper layer, there was no increase in temperature at a low airflow rate, which indicated that cooling naturally was insignificant in this case due to

ent on the PCB. When the flow velocity of the blown air was less than 16 ୫

From a practical point of view, the existing equipment cooling systems produced a

Let us consider models with a copper layer (Figure 10) and graphs of the component temperature dependence on the input flow rate (Figure 11). The introduction of a copper layer significantly reduced the temperature of the component and the temperature gradi-

. However, in real conditions, the airflow velocity in elec-

ୱ . Therefore, the following conclusion could be

ୱ

, the tempera-

**Figure 10.** Heat distribution according to models with a copper layer under natural convection conditions: (**a**) surface component; (**b**) embedded component. **Figure 10.** Heat distribution according to models with a copper layer under natural convection conditions: (**a**) surface component; (**b**) embedded component. *Micromachines* **2022**, *13*, x FOR PEER REVIEW 10 of 12

**Figure 11.** Dependence of the component temperature on the airflow rate in models with a copper layer. **Figure 11.** Dependence of the component temperature on the airflow rate in models with a copper layer.

**4. Conclusions**  Currently, the study of the physical reliability of electronic devices and the development of new methods to improve it are significant, especially for thermal processes. For these purposes, we created a simulation model of heat distribution on a printed circuit board, which took into account the processes of conductive and convective heat exchange. Also, in models with a copper layer, there was no increase in temperature at a low airflow rate, which indicated that cooling naturally was insignificant in this case due to the slight difference between the temperatures of the PCB and the environment. Supplementary Materials for the article include the results of simulation of heat distribution by models under conditions of forced convection at different airflow rates.

#### The reliability of the model was confirmed by experiment. Then, based on the constructed model, we investigated the dependence of the com-**4. Conclusions**

ponent temperature on the cooling method (natural convection and forced convection with variation of the air flow). The results showed that the temperature of the embedded component was less than the temperature of the surface-mounted component under natural convection and, in most cases, of forced convection. The surface component cooled better under conditions of super-efficient forced convection, which are usually not avail-Currently, the study of the physical reliability of electronic devices and the development of new methods to improve it are significant, especially for thermal processes. For these purposes, we created a simulation model of heat distribution on a printed circuit board, which took into account the processes of conductive and convective heat exchange. The reliability of the model was confirmed by experiment.

able in real equipment. To do this, the airflow velocity needed to be greater than 16 ୫ ୱ . The simulation results showed that the temperature of the component, practically, did not depend on the installation method using a copper polygon. However, in modern Then, based on the constructed model, we investigated the dependence of the component temperature on the cooling method (natural convection and forced convection with variation of the air flow). The results showed that the temperature of the embedded

multilayer printed circuit boards, polygons function as power supply routes. They are

It is important to note that the embedded component had the most significant advantage in the absence of forced cooling, which is typical for embedded systems. Thus, the embedded mounting technology reduced the temperature of the component. This phenomenon reduced the thermal effect of the component on the entire electronic device, which increased its overall reliability [29]. However, the question of the thermal efficiency of the integrated mounting with a high density of components on the inner layers of the printed circuit board remains open. In addition, in order to obtain a complete picture of the reliability of PCBs with embedded components, additional studies are necessary using

**Supplementary Materials:** The following supporting information can be downloaded at: https://ze-

**Author Contributions:** Conceptualization, F.V. and M.K.; methodology, F.V.; software, M.K.; validation, F.V. and V.M.; formal analysis, F.V.; investigation, M.K.; resources, F.V.; data curation, F.V. and V.M.; writing—original draft preparation, M.K.; writing—review and editing, V.M. and F.V.;

nodo.org/record/6301748#.YhwXqehBzct (Accessed 16 April 2022).

would further worsen its cooling.

conditions of vibration, humidity and dust.

component was less than the temperature of the surface-mounted component under natural convection and, in most cases, of forced convection. The surface component cooled better under conditions of super-efficient forced convection, which are usually not available in real equipment. To do this, the airflow velocity needed to be greater than 16 m s .

The simulation results showed that the temperature of the component, practically, did not depend on the installation method using a copper polygon. However, in modern multilayer printed circuit boards, polygons function as power supply routes. They are located in the middle of the PCB package, but not on its surfaces. In this case the surface component could be connected to the polygons only through the transition holes, which would further worsen its cooling.

It is important to note that the embedded component had the most significant advantage in the absence of forced cooling, which is typical for embedded systems. Thus, the embedded mounting technology reduced the temperature of the component. This phenomenon reduced the thermal effect of the component on the entire electronic device, which increased its overall reliability [29]. However, the question of the thermal efficiency of the integrated mounting with a high density of components on the inner layers of the printed circuit board remains open. In addition, in order to obtain a complete picture of the reliability of PCBs with embedded components, additional studies are necessary using conditions of vibration, humidity and dust.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //zenodo.org/record/6301748#.YhwXqehBzct (Accessed 13 April 2022).

**Author Contributions:** Conceptualization, F.V. and M.K.; methodology, F.V.; software, M.K.; validation, F.V. and V.M.; formal analysis, F.V.; investigation, M.K.; resources, F.V.; data curation, F.V. and V.M.; writing—original draft preparation, M.K.; writing—review and editing, V.M. and F.V.; visualization, M.K.; supervision, F.V.; project administration, F.V.; funding acquisition, F.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was performed within the framework of the state assignment of the Ministry of Education and Science (Russia), topic № FSFF-2020-0015.

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Arable Weed Patterns According to Temperature and Latitude Gradient in Central and Southern Spain**

**María Luisa Gandía, Carlos Casanova, Francisco Javier Sánchez, José Luís Tenorio and María Inés Santín-Montanyá \***

Environment and Agronomy Department, National Institute for Agricultural and food Research and Technology (INIA), 28040 Madrid, Spain<EMAIL_ADDRESS>(M.L.G<EMAIL_ADDRESS>(C.C<EMAIL_ADDRESS>(F.J.S<EMAIL_ADDRESS>(J.L.T.)

**\*** Correspondence<EMAIL_ADDRESS>
Received: 1 July 2020; Accepted: 11 August 2020; Published: 13 August 2020

**Abstract:** (1) Background: In agro-ecosystems, the success of the crops has a strong connection to biodiversity in the landscape. In the face of climate change, it is important to understand the response to environmental variation of weed species by means of their distribution. In the last century, biodiversity has been impacted due to a variety of stresses related to climate change. Although the composition of vegetation tends to change at a slower rate than climate change, we hypothesize species present in weed communities are distributed in diverse patterns as a response to the climate. Therefore, the general aim of this paper is to investigate the effect of temperature, using latitude as an indicator, on the composition and distribution of weed communities in agro-ecosystems. (2) Methods: Weeds were monitored in georeferenced cereal fields which spanned south and central Spanish regions. The graphic representation according to latitude allowed us to identify groups of weeds and associate them to a temperature range. We classified weeds as generalist, regional, or local according to the range of distribution. (3) Results: The monitoring of species led to the classification of weeds as generalist, regional or local species according to latitude and associated temperature ranges. Three weed species that were present in all latitude/temperature regions, were classified as generalist (*Linaria micrantha* (Cav) Hoffmanns & Link, *Sonchus oleraceous* L., and *Sysimbrium irium* L.). The species were classified as regional or local when their presence was limited to restricted latitude/temperature ranges. One weed, *Stellaria media* (L.) Vill., was considered a local species and its distribution dynamics can be considered an indicator of temperature. (4) Conclusions: The novel methodology used in this study to assign weed distribution as an indicator of climatic conditions could be applied to evaluate climate gradients around the world.

**Keywords:** agro-ecosystems; biodiversity; climate change; weed communities

#### **1. Introduction**

Biodiversity plays an important role in maintaining the processes and functions of ecosystems, including farming [1,2]. Plants respond to environmental conditions based on their needs in the habitat and physiological tolerances, which influences the composition of weed communities, their structure and resilience. In the last century, biodiversity has been impacted due to a variety of stresses related to climate change which have affected how and where species live, reproduce, and interact with each other [3,4]. The composition of vegetation tends to change at a slower rate than climate change, but nevertheless species present in weed communities have been distributed in diverse patterns according to their response to the climate [5].

Because climate change has influenced the annual productivity of many crops [6,7], as well as the composition and distribution of weed communities in agro-ecosystems [8,9], we can say that climate change presents both a threat to biodiversity and a cost in terms of weed control, for agriculture.Weeds are one of the main limiting factors of crop production worldwide, with part of their success being due to their plasticity: weed flora can adapt to changes in the environment [10–13]. This plasticity may respond to climate change at the local and regional level [14–16], and can act as a filter to soften the negative effects of a rapid change of climatic conditions in a particular area. In the short term, plasticity in the weeds allows them to cope with changes of environmental conditions.

Most of the agronomic actions in an agro-ecosystem has been designed to reduce the overall weed density [17–19]. However, the sustainability of cropping systems should be assessed not only in terms of crop yield but also adequate levels of biodiversity within cropland [20]. Agricultural practices, which vary according to local climate, cause a disturbance to the agro-ecosystem, and change the dynamics of the weed community, creating ecological niches [2,5,21,22]. These niches formed by weed communities affect plant diversity in the field [23,24]. Extreme weather combined with agronomic practices can lead to empty niches which present an opportunity for the establishment of new weed species [25,26]. The way that these niches are filled depends on the level of biodiversity. In agro-ecosystems with a greater diversity of weeds, there are more species available to fill these niches, which prevents any one species becoming dominant [27–29]. Logically, in agro-ecosystems with less diversity, it is easier for one weed species to take over a niche created by agricultural activity and become dominant. Weed diversity provides ecosystems services for the upper trophic levels in cropland ecosystems [23,30]. These changes in distribution of the weed community (abundance and richness) are often difficult to predict so monitoring changes is recognized as crucial both in stable and unstable environments.

Studies on global warming have predicted a change in general temperature and rainfall patterns [33,34], but there are high levels of uncertainty about the nature of local changes. In Spain, temperature trend analysis confirms that there has been a widespread rise in annual average temperature since the mid-1970s, with warming being more apparent in winter (1.9 ◦C), and this increase in temperature in winter has given rise to longer growing seasons. One of the least explored aspects of global warming is its possible impact on the geographic distribution of agricultural weed species [35,36]. Climate change, and the consequent longer growing seasons, has led to the appearance of weed species which are more common in warmer conditions [24,37,38]. In this context, we have seen that the weed distribution patterns found in agro-ecosystems vary according to latitude, as influenced by temperatures gradients.

We hypothesize that climatic conditions are linked to weed species distribution.Although, studies have been conducted on the effects of climatic conditions on weeds, to our knowledge, relatively few studies have focused on weed distribution on regional and local scales. The objectives of this study were: (1) to implement a novel methodology; (2) to analyze the distribution pattern of weed species with respect to climate gradients and identify weed species as potential indicator of climate changes; and (3) to provide data regarding expected weed distribution changes due to global warmer for agricultural managers, in order to maximize yield parameters by maintaining biodiversity.

#### **2. Material and Methods**

In this study, we identified the weed species in cereal agro-ecosystems found in UTM quadrants of 10 km2 (grid zones 29 and 30+, from latitude bands S and T). These quadrants were established within a North-South latitude range between Madrid (central Spain) 40.51 ◦N and Seville (southern Spain) 37.24 ◦N. We categorized plant species by latitude in a total of 50 quadrats.

This geographical range covers three latitudinal communities. The first two communities, located within Madrid and Castile La Mancha regions, are under Central Iberian plateau conditions. The altitude of the plateau is responsible for the existence of a continental Mediterranean climate. The most significant characteristics in these areas are severe winters, hot summers, summer drought, irregular rainfall, strong thermal oscillations and remarkable aridity. These features have been the result of the interrelations

between geographical factors such as latitude, the situation of the region within the Iberian Peninsula, the relief layout and altitude. The annual thermal amplitude (difference between the average temperature of the coldest month and the hottest month) is very high, normally between 18 and 20 ◦C due to continentality. In July, the average monthly temperature is above 24 ◦C in most of the regions. The southern-most latitudinal community in the study, Andalusia region (Seville), has also semiarid conditions, similar to the continental Mediterranean climate but with more temperate conditions due to the coastal proximity.

The weed monitoring was carried out on georeferenced cereal fields, in the established route, during April 2018. The "time window" for the monitoring was decided according to the crop maturation stage. The cereal booting stage was selected because it is one of the critical competition stages in cereals [39], and the best time to identified weed flora representative of agro-ecosystems. All of the fields monitored, from central Spain were between 600 and 800 m in altitude and southern Spain fields were at an altitude range from 6 to 400 m. All the fields monitored showed the standard conventional management for each region.

To display the weed distribution along the latitude gradient a novel ordination methodology was performed on weed species present in the time of monitoring. A binary code was assigned to the presence or absence of weed species. The data was presented according to latitude (North–South) and the Tmax and Tmin were ordered from lowest to highest. Then, we represented these weed arrangements graphically (see the figures): where the peaks on the chart indicate the presence of weed species in a certain latitude or temperature. Also, the Spearman rank order correlation (Supplementary Materials) was used to obtain the relation between the weed species ordered by latitude and temperatures at the time window for monitoring, in the month of April.

In terms of temperatures, average maximum and minimum temperatures in April from the WorldClime database were reviewed, and data were adjusted to sea level. This method allowed us to classify and catalogue weed species according to environmental conditions, specifically different temperatures. This data analysis allowed for the identification of weeds that are able to tolerate high temperatures, these species, known as thermophilic, were found in latitude ranges that correspond to higher average temperatures. Other species were found not to thrive in the same conditions and weeds that are particularly sensitive to low temperatures and therefore have a reduced range of distribution were identified.

#### **3. Results**

A total of sixty-six weed species were observed in agro-ecosystems along our established route. The results of the process of categorizing weed species provided valuable information about distribution of weeds according to latitude and temperature. For the timing of weed monitoring, April 2018, the correlation between latitude and Tmax and Tmin were 0.749 and 0.790, respectively (in Supplementary Materials).

Figure 1 represents the findings of the sampling surveys. The peaks in the graph show presence of the weed species in the corresponding area. For this graphic representation we classified the weeds according to latitude. We can see three latitude ranges of 3 degrees (from 40.5◦ to 37.2◦ ) which coincided with three regions of Spain called latitudinal communities: Madrid, Castile La Mancha, and Andalusia. We classified the weeds as local, regional and generalist, according to their range of dispersion:

(1) Local weed species were defined as restricted to a single latitudinal community (either Madrid, Castile La Mancha, or Andalusia), less than 33% of the geographical range.

(2) Species were classified as regional (two latitudinal communities) if they had a higher dispersion than local, between 33 and 66% of the geographical range.

(3) Generalists weeds were found in all areas of study, over three latitudinal communities, over 66% of the geographical range.

**Figure 1.** Graphical pattern of arable weed species distribution found in the studied areas according to latitude from Central to South of Spain (the peaks show presence of the weed species in the corresponding area).

Weed species were also divided into four groups by their thermal amplitude (species that appeared in up to 18, 36, 54 and 100% of Tmax and Tmin ranges), Figures 2 and 3 illustrate this. Figure 2 categorizes weed species according Tmax in April. The Tmax range was 4.7 ◦C (from 20.9 ◦C to 25.7 ◦C), and Figure 3 classifies weed species observed within a Tmin range of 4.1 ◦C (from 10.2 ◦C to 14.3 ◦C). The weed species were divided into four groups according to temperature: (1) weed species dispersed within a narrow temperature range, less than 18% of the total range; (2) weed species which were found within the 18–36% of temperature range; (3) weed species with more thermal amplitude, between 36 and 54%, and finally, (4) weed species present in all Tmax ranges. The classification of local, regional and generalist species had different results according to each of the three figures. We considered a species to be an indicator of temperature if its classification was the same in all three figures.

**Figure 2.** Graphical pattern of arable weeds distribution according to the Tmax in April.

**Figure 3.** Graphical pattern of arable weeds distribution according to the Tmin in April.

According to latitude, *Anchusa azurea* Mill. and *Avena sterilis* L. were local species in Andalusia. *Moricandia arvensis* (L.) DC., *Eruca vesicaria* (L.) Cav. and *Malcomia africana* (L.) R.Br. were local species in Castile La Mancha. We consider these species to be potential indicators because their latitude ranges were narrow (≤33% of range) and also their dispersion of Tmax and Tmin range were between 18 and 36% (Figures 2 and 3, respectively). Other local species found were *Cardaria draba* (L.) Desv., *Blugossoides arvensis* (L.) I.M. Johnst., *Silene vulgaris* (Moench) Garcke, *Raphanus raphanistrum* L., and *Lamium amplexicaule* (L.). All of them were recorded in the latitude community of Madrid, 40◦ of latitude-north, with distribution

within a geographical range corresponding to 33%, and 18% of dispersion in Tmin range. Local species were distributed in narrow ranges in terms of latitude and minimum temperature, however, a rise in average Tmin could favor their spread into more northern geographical ranges.

*Linaria micrantha* (Cav.) Hoffmanns. and Link. appeared as regional species, between 33 and 66% of dispersion within latitude range, and the variation in Tmax and Tmin ranges were narrower, ≤18%. In climate gradients where average temperatures are too high or too low, this weed species was not present. Then, global warming could completely change the geographical distribution of this weed species. *Sysimbrium irio* L. was observed as regional species, with similar variation in latitude and Tmin, but its Tmax range was very low (≤18%). When the maximum temperature is too high this weed species was not present. The opposite occurred with *Sonchus oleraceous* L., similar latitude and Tmax ranges variations, and Tmin range of ≤18%. When the average temperature was too low, this weed species was not present.

*Stellaria media* (L.) Vill. appeared in the highest latitude range (66–99%), but the narrowest Tmax and Tmin ranges (≤18%). We can state that this generalist weed is sensitive to temperature.

#### **4. Discussion**

Some weed species can share response patterns to particular environmental circumstances and hence affect their geographical distribution. Global warming, which implies longer growing seasons, may favor the appearance of weed species in regions which used to be colder [24,38]. Weeds sensitive to temperatures such as thermophilic weeds, or species with late emergence and opportunistic species can thrive now in some farming systems due to a rise in temperatures [5,8,9]. Numbers of these weed species have been increasing in northern areas because they are able to adapt to warmer conditions, and the growth cycle of these weeds has changed, accelerating the flowering stage [36–38,40–42].

On the other hand, climate change indirectly influences weeds that adapt to different agronomic practices. Frequently, we find that weeds are closely associated with the cropping system, and climate conditions have, therefore, an influence on the occurrence of weeds through crop management and land use [18,19]. From the above, we can deduce that the weeds that are present today in the agro-ecosystems are not necessarily the weeds that will be of concern in the future. However, further compilation of data regarding climatic conditions and the identification of weed flora in a determined area is necessary [26].

In this paper, we propose the implementation of a monitoring program for weeds in cereal systems, at a regional or local scale. It is important to facilitate the detection of changes within agro-ecosystems and allow farm managers the chance to predict the effects of climate change and reduce the impact on crop parameters. However, there is little information of the distribution of weed species [24,43], within the cereal agro-ecosystems, according to variables as latitude and temperature changes. Our study about weed distribution offers a resource to observe and compare weed species distribution in semiarid agro-ecosystems at the local level.

We found *Anchusa azurea* Mill., *Avena sterilis* L., *Moricandia arvensis* (L.) DC., *Eruca vesicaria* (L.) Cav., and *Malcomia africana* (L.) R.Br. as local species. We could consider these species as marker of warm conditions. If average minimum and maximum temperature increase, can be facilitated a thermophilic movement of these species and we would find these weeds relocated in other latitudes. *Avena* spp. has been categorized as aggressive grass in northern countries [12]. *Avena sterilis* L. is a widely spread weed by the Iberian Peninsula and has been principally controlled by herbicides. Its presence in the monitoring reflects its ability to adapt to warmer conditions. Therefore, the dispersal mechanisms of these species will have a great influence on their distribution and the plant traits of these local species should be object of future consideration.

*Cardaria draba* (L.) Desv., *Blugossoides arvensis* (L.) I.M. Johnst., *Silene vulgaris* (Moench) Garcke, *Raphanus raphanistrum* L., and *Lamium amplexicaule* L., were local species disperse in Tmin range narrowest. So, we think that Tmin can sorted the weed species within the observed latitude. Some authors [44,45] have found as local species *Silene noctiflora* due to propagule transport mainly relies on biological dispersal mechanisms and the habitat fragmentation prevent the dispersal of species.

*Linaria micrantha* (Cav.) Hoffmanns. and Link., *Sysimbrium irio* L. and *Sonchus oleraceous* L. were species more spread than others mentioned in this study. However, too low or too high temperatures may challenge their survival within their latitude, and so we could consider these three weeds (*Linaria micrantha*, *Sysimbrium irio,* and *Sonchus oleraceous*) as opportunistic because their presence is a function of optimal temperatures. Several *Sysimbrium* spp. species has been found in oilseed rape fields in Germany [38]. *Stellaria media* (L.) Vill. would also be considered a generalist species, widespread in all latitudes, but sensitive to changes in temperatures. *Stellaria media* (L.) Vill., as a nitrophilous species with shading tolerance has become relevant species in northern areas [12,46].

Temperature change reveals both the threat to biodiversity and the cost of weeds for agriculture. Because temperature has influenced the annual productivity of many crops, as well as the composition and distribution of weed communities in agro-ecosystems. These results demonstrate some degree of plasticity in response to environmental variation of weed species by means of their spatial distribution. Of course, the plants are integrated in agro-ecosystems, and these responses are influenced by other factors such as agronomic practices.

In view of the results, climate change poses an uncertainty about the best way to design weed management strategies. A static style of management cannot be assumed any longer, and an adequate management of weeds, in the future, must take into account temperature change, land use, and human activity. Changes in the composition of the weed community can reduce the effectiveness of existing control strategies, as well as yield and economic cost to producers due to uncontrolled weeds. Also, important ecosystem services provided by weeds can be compromised if the composition of the community evolves with climate change. The different weed distribution patterns found could, in the long term, lead to variations of ecosystem functions. Therefore, the estimation of the damage of the weeds in the agro-ecosystems will be very important to reduce its impact and develop management strategies, current and future, effective against climate change.

#### **5. Conclusions**

Our research supports the common view that the monitoring of biodiversity is a means to obtain information on the state and dynamics of the agro-ecosystem. The application of the novel methodology proposed has made it possible to visualize clear links between latitude and temperature ranges related to weed distribution. The monitoring method presented here can be a promising tool to supply information in bioclimatic distribution models of species that needs to be validated with empirical data on weeds under changing climatic conditions. Also, this methodology proposed may be applied to the study of climate gradients around the world.

We think that there are temperature sensitive weeds which can be used in further studies as indicators of climate change by comparing distribution to local and regional data. Considering that weed community changes are not always noticeable in the short term, we recommend establishing long-term monitoring to detect changes in the biodiversity of agro-ecosystems. Furthermore, any changes in weed distribution in the agro-ecosystem due to temperature changes would affect the crops. Therefore, any information about shifts of weed dynamics related to temperature changes is going to be especially important in the future for crop management.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-4433/11/8/853/s1, Figure S1: Distribution of weed species according Latitude (from South to North in *X*-axis) and temperatures. Spearman rank order correlation values with temperatures in April month = 0.749 (Tmax) and 0.7906 (Tmin).

**Author Contributions:** All authors contributed to the monitoring of species. M.I.S.-M. and M.L.G. performed the original draft preparation of the manuscript; M.I.S.-M. and C.C. conceived and designed the experiments and revised the whole manuscript; M.L.G., J.L.T., and F.J.S. contributed to the analysis de data and the design of the experiments. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work has been funded by projects AT2017-003 and RTA2017-00006-C03-01 (Spanish Ministry of Science and Innovation) and by Biodiversity Foundation of Spain (Ministry for the Ecological Transition) and INIA by means of the project PRCV00590 ("The role of resilience in the composition of weed communities of cereal agro-ecosystems. Adaptive responses of flora to climate change").

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


*2.3. Data Analysis*

Only continuous strides walked along straight paths were considered for the analysis, excluding turns and freezing episodes. Gait events (i.e., foot contact and foot o ff) were automatically identified from the angular velocity around the medio-lateral axis of the shank [35]. For each subject, stride time was calculated as the di fference between two consecutive foot contacts of the same leg.

For the assessment of gait symmetry, the di fference between left and right leg of stride time sequences was tested per subject using the Kruskal–Wallis test (statistical significance 5%), since normality of distribution was not verified (Shapiro–Wilk test).

sEMG data were bandpass filtered at 20–450 Hz [36,37], then processed by a double threshold statistical detector to provide the onset and o ffset time instants of TA and GM activity [38]: per muscle, an amplitude threshold ζ and a numerosity threshold *r*0 were defined; if at least *r*0 out of m consecutive samples, in absolute value, are above ζ, activation is considered on and set to 1; elsewhere

activation is considered OFF and set to 0. On-set instants are identified with transitions of the activation from 0 to 1, o ff-set from 1 to 0.

The behavior of the double-threshold detector is determined by three parameters: the amplitude threshold ζ, the numerosity threshold *r*0, and the length of the observation window m. The values of ζ and *r*0 are statistically selected to minimize the value of false-alarm probability and maximize probability of detection for specific signal-to-noise ratio (SNR) and background noise. To guarantee the performance of the threshold detector, only signals with a minimum SNR value of 10 were considered; the value was chosen according to literature [38]. The values of the background noise level and the SNR were estimated using the statistical approach proposed by Agostini et al. [39]. The length duration of the observation window m was set to 60, i.e., 30 ms, as suitable value for the study of muscle activation in gait analysis [38].

Muscle activation events and intervals (o ff-set—on-set) per gait cycle were normalized with respect to the corresponding gait cycle duration, then, the number *n* of times the muscle was activate within a single gait cycle was calculated to define the n-activation pattern per gait cycle and per muscle.

To quantify the frequency of occurrence of each n-activation pattern, muscle activations of each muscle were gathered according to the number of detected intervals within each gait cycle and the occurrence frequency of the single n-activation pattern was calculated per muscle and per subject as:

$$\text{Occurrenze Frequency} \ (n) = \frac{\text{Number of guit cycle with activation pattern}}{\text{Total number of guit cycle}}$$

Then, mean and standard dispersion (SD, i.e., standard deviation divided by the square root of the number of strides) values of occurrence frequency of each n-activation patter of each muscle were calculated over subjects.

To characterize the timing of each n-activation pattern, mean and SD of normalized activation events were calculated per n-activation pattern, per muscle, and per patient.

For the co-activation investigation, only patients showing a SNR greater than 10 for both muscles in at least one of the two limbs were considered.

To characterize co-activation [4], per GM n-activation pattern:

Coactivation was identified when both concurrent GM and TA normalized n-activation patterns were above 0.1. Matlab R2018a (MathWorks BV, USA) was used for data analysis.


Marine Ecosystems - Biodiversity, Ecosystem Services and Human Impacts

*Edited by Ana Marta Gonçalves*

Published in London, United Kingdom

Marine Ecosystems - Biodiversity, Ecosystem Services and Human Impacts http://dx.doi.org/10.5772/intechopen.95153 Edited by Ana Marta Gonçalves

#### Contributors

Abhijit Mallik, Suchismita Prust, Puja Chakraborty, Shyamal Chandra Sukla Das, Shashi Bhushan, Adib Ali Saad, Lana Ezaldin Khrema, Melekote Nagabhushan Arun, Rapolu Mahender Kumar, Banugu Sreedevi, Guntupalli Padmavathi, Pallakonda Revathi, Neha Pathak, Dayyala Srinivas, Raman Meenakshi Sundaram, Sailaja Nori, Rajuddin Syamsuddin, Ana Marta Gonçalves, Andreia Filipa Mesquita, Fernando José Mendes Gonçalves, Neila Annabi-Trabelsi, Mohammad Ali, Genuario Belmonte, Habib Ayadi, Wassim Guermazi

#### © The Editor(s) and the Author(s) 2024

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Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

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**2. Need for dry etching**

A serious limitation of suspended MEMS structures is that they tend to deflect through stress gradient or surface tension induced by trapped liquids during the final rinsing and drying step. Problems like stiction and bridge collapse are associated with producing a free standing structure. The stiction is described as a process of bonding the top and bottom

Plasma Based Dry Release of MEMS Devices 271

difficult to estimate because of the variation in deposition process or the density of

The liquid bridging occurs due to the surface tension of the trapped capillary liquids. The drying of this trapped liquid is difficult due to the presence of concentrated soluble impurities. These trapped impurities increase surface tension while decreasing the vapor pressure. A third possible adhesion cause can occur if suspended membrane is placed in contact with the lower contact surface due to some external force. This adhesion can occur due to deliberate placement of collapsing forces or can be due to shock effect (Mastrangelo,

The removal of sacrificial layer to achieve a suspended microstructure is the final step in the surface micromachining process. This process mostly requires a wet etching for removal of sacrificial layer. In some cases the removal is also done using plasma etch when sacrificial layer is other than a metal layer like polyimide or photo-resist. After the wet etching the microstructure is rinsed using DI water to remove the residues left during the etching. When the microstructure is pulled out of DI water a strong capillary force develops. A meniscus forms at the interface under the microstructure when the microstructure is pulled out of water. The curved interface creates a pressure called Laplace pressure which is given

*L l*

a definite angle of contact between the liquid and the substrate as shown in figure 1.

*P*

*a b*

*r r*

The liquid surface tension is denoted by γ*l* and two radii of curvature of liquid surfaces are given as *ra* (parallel to surface normal of the substrate) and *rb* (in the plane of the substrate). In most cases, the liquid droplet on the surface of the substrate will not wet it. It will present

(a) (b)

In equilibrium condition, the contact angle between liquid and solid is determined by the balance between the surface tension of the three interfaces. The contact angle *θ* at the junction of three interfaces is defined as the angle formed between solid-air, liquid-air and liquid-solid interfacial tensions in equilibrium. The contact is given by the Young's equation

>

Fig. 1. Contact angle at solid liquid interface of (a) non-spreading (b) spreading liquid

 *SA SL LA*

1 1 (1)

*cos* 0 < *θ* < *π* (2)

deposited material. In any case, the adhesion strength tends to be significant.

2000).

**3.2 Stiction due to capillary forces**

by (Israelachvili, 1991)

(Israelachvili, 1991) as

electrodes together by a microscopic surface due to the planner nature of the electrodes. Stiction of MEMS is a common concern. When a sacrificial layer is removed and rinsed in deionized water, the surface tension of rinse water pulls the delicate micro structure to the substrate as the wafer dries. Risk of stiction is caused by the capillary forces originating from the dehydration of meniscuses, van der Waals force or the electrostatic force formed between the suspended beam structures and the substrate following the wet etching (Madou, 2002). These forces keep the structure firmly attached with the substrate. Stiction remains a reliability issue due to contact with adjacent surfaces after release.

Stiction is an inevitable problem we deal with for achieving the working RF MEMS devices. With increase in cantilever length, its flexibility perpendicular to the substrate increases which also increases the susceptibility to stiction. When the structure gets attached with the substrate due to stiction, the mechanical force required to dislodge it from the surface is large enough resulting in damage to MEMS structure (Modou, 2002). The surface morphology has a strong influence on stiction and is a serious problem particularly in metal to metal contact switches (Varadan et al., 2003).

In order to achieve a released structure, contact between the structural elements and the substrate should be avoided during processing. Etching can be done by physical damage, chemical damage or combination of both. Release of these suspended beam structures can be done either through wet etching or dry etching. Etching in a plasma environment has several advantages as compared to wet etching. In the wet etching, this may become impossible or very difficult due to large surface tension forces. Moreover, if a MEMS structure is left too long in the etchant, the structure can be over etched and damaged (Harsh et al., 1999). Plasmas are easier to start and stop than simple immersion wet etching (Campbell, 1996). Also sensitivity of plasma etch is less prone to small changes in the temperature of the wafer. Above mentioned factors make plasma etching more repeatable than wet etching.

Different techniques over a period of time have been used to avoid stiction. Method of creating stand-off bumps on the underside of a polysilicon plate was introduced (Abe et al., 1995) which added meniscus shaping microstructures to the perimeter of the microstructure for reducing the chance of stiction. To avoid stiction critical point drying technique using CO2 dryer is used (Chan et al., 2007) to release the structures.