Temporal laser cavity-solitons for micro-resonator based optical frequency combs
Precise timing has led to many advances, such as GPS and the Internet, which depend critically on frequency and time standards. The currently limited accuracy, however, is hindering the progress towards societal-changing technologies such as telecommunications beyond 5G or precise earth mapping.
Optical atomic clocks based on optical frequency combs – Nobel prize in Physics, 2005 to Hall and Hänsch – are the only technology capable of providing timing accurate up to 10^(-18) seconds, answering such a demand of time precision. The realisation of such clocks in portable scale is expected to change the technology landscape.
Micro-combs – based on miniature optical resonators – have galvanized the attention of the world over the past ten years with the promise to realise the full potential of frequency combs in a compact form. 
However, these devices still do not meet the demand of practical atomic clocks which require reliable optical sources and currently depend on bulky pulsed lasers, which are well-known for their robustness but unfit for portable applications.
Developing energy-efficient micro-combs with the reliability and versatility of control of modern pulsed lasers will require to surpass the intrinsic limitations of the nonlinear physics exploited so far for their generation.
Here we propose a high-gain/ high-risk research plan which steers from the state-of-the-art and builds on a different physics for developing micro-combs with control capabilities beyond the current miniature solutions.
Specifically, we will exploit the generation of localised waves called temporal laser cavity-solitons in complex resonators exhibiting lasing and parametric nonlinear interactions. Such a setting is mostly unexplored and this proposal will demonstrate the unique features of these waves and their general impact in broader physics. Eventually, this study will pave the way to a class of robust micro-combs which can be controlled with user-friendly machine learning approaches.