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CHEN Hui-bin, TANG Kai-fei, YOU Zhen-yu. Fully complex optical neural network with insertion-loss robustness[J]. Chinese Optics. doi: 10.37188/CO.2023-0198
Citation: CHEN Hui-bin, TANG Kai-fei, YOU Zhen-yu. Fully complex optical neural network with insertion-loss robustness[J]. Chinese Optics. doi: 10.37188/CO.2023-0198

Fully complex optical neural network with insertion-loss robustness

doi: 10.37188/CO.2023-0198
Funds:  Supported by the National Natural Science Foundation of China (No. 61705119)
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  • Linear optical processors based on the cascading of Mach-Zehnder Interferometer (MZI) topologies have been demonstrated to be an important way of implementing optical neural networks, but several practical challenges still need resolution. To solve problems arising from chip manufacturing and testing processes that could lead to phase errors and insertion losses, we conducted experiments and theoretical simulations for various reconfigurable optical processors. We found that using arbitrary unitary matrices instead of arbitrary matrices achieved by a single N×N Clements unit 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, due to the limited degrees of freedom, we introduced a phase-shift layer before each layer of the Clements unit. Particularly in multi-layer optical neural networks, this design aids in mapping classification data to higher-dimensional spaces, facilitating faster neural network convergence.


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