Neuromorphic Photonic refers to the application of photonic systems designed to mimic the information-processing capabilities of the human brain. This involves using optical components, such as silicon micro-ring resonators, to perform tasks similar to neural networks, leveraging the inherent speed and efficiency of light for data processing. The focus is on developing systems that can efficiently handle signal processing or neural network inference while minimizing issues like thermal crosstalk, which can degrade performance. By utilizing passive thermal desensitization mechanisms, neuromorphic photonic systems aim to enhance responsiveness and accuracy, making them suitable for advanced computing applications.

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Low-Latency Passive Thermal Stabilization of a Silicon Micro-Ring Resonator with Self-Heating

Analog photonic information processing can be implemented with low chip area using wavelength-division multiplexed systems, which typically manipulate light using micro-ring resonators. Micro-rings are uniquely susceptible to thermal crosstalk, with negative system performance consequences if not addressed. Existing thermal sensitivity mitigation methods face drawbacks including high complexity, high latency, high digital and analog hardware requirements, and CMOS incompatibility. Here, we demonstrate a passive thermal desensitization mechanism for silicon micro-ring resonators exploiting self-heating resulting from optical absorption. We achieve a 49% reduction in thermal crosstalk sensitivity and 1 ?s adaptation latency using a system with no specialized micro-ring engineering, no additional control hardware, and no additional calibration. Our theoretical model indicates the potential for significant further desensitization gains with optimized microring designs. Self-heating desensitization can be combined with active thermal stabilization to achieve both responsiveness and accuracy or applied independently to thermally desensitize large photonic systems for signal processing or neural network inference.