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To counter the exponential growth of computing power requirements for machine learning, efficient sailing of the integrated photonic processors has become a fundamental issue to be addressed. However, it remains challenging to properly calibrate the circuit imperfections, such as fabrication errors and crosstalk originating from both thermal and electric effects, which drastically affects the performance as the circuit size becomes larger. We demonstrate a silicon-photonics 16×16 Clements-type photonic vector-matrix multiplier. The degradation of fidelity caused by crosstalk and fabrication error was successfully compensated using our proposed machine learning based tuning method and deterministic calibration. The first experimental 10-digit MNIST classification was performed, which defines the classification results directly corresponding to the optical output ports. Furthermore, we also fabricated an 8 8 MZI-mesh photonic processor based on the planar lightwave circuit (PLC) technique which can realize low wavelength dependence operation due to low fabrication errors. This structure achieves the efficient throughput due to the O(N2W) operation, where N and W denote the number of spatial and wavelength channels, respectively. A high fidelity of 0.99 at 1550 nm and >0.96 over the C band was achieved, demonstrating the feasibility of the matrix-matrix multiplication operation with a combination of MZI-mesh and WDM.
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