插损鲁棒性的全复值光学神经网络OA北大核心CSTPCD

Linear optical processors based on the cascaded topology of Mach-Zehnder Interferometer(MZI)have been demonstrated to be an important way of implementing Optical Neural Networks(ONN),but sever-al practical challenges still need resolution.Concerning issues arising from chip manufacturing and testing processes that could lead to phase errors and insertion losses,we conducted experiments and theoretical sim-ulations for various reconfigurable optical processors.We found that the weights of any arbitrary unitary mat-rix can be realized through some single N×N Clements units,that can substantially reduce the optical depth and enhance robustness against insertion losses.This approach allows for the construction of fully complex optical neural networks.Additionally,In multi-layer ONN,due to the limited degrees of freedom in con-structing this arbitrary matrix,we introduced a phase-shift layer before each layer of the Clements unit.This design aids in mapping classification data to higher-dimensional spaces,facilitating faster neural network convergence.