Photonic neuromorphic learning via generalized in situ physical gradient descent

Summary

Physics-based neuromorphic computing promises major gains in performance and energy efficiency, but training remains largely in silico, requiring costly physical modeling and suffering from fabrication-induced errors. Here we introduce on-chip, in situ physical gradient descent (INSPIRE), a general training method for photonic integrated circuits. INSPIRE uses on-chip synthetic time-reversal holography to measure the full complex fields of bidirectional photonic modes, enabling gradient computation and parameter updates directly in the physical system. The framework is topology-agnostic and compatible with diverse optical circuits. Experiments yield trained matrices with a relative error of 0.26%, and we demonstrate in situ training through scattering media for matrices larger than the number of native tunable elements. INSPIRE also enables meta-photonic circuits to perform in situ meta-learning, realizing single-shot photonic learning with 251-fold model compression and 136-fold task-specific training acceleration. This work offers a practical route toward adaptive and efficient intelligent photonic systems. This study reports a physics-based in situ learning method that trains artificial neural networks directly within the photonic integrated circuits, turning diverse optical circuits into efficient AI learning hardware.