Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
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Optical machine learning offers advantages in terms of power efficiency, scalability and computation speed. Recently, an optical machine learning method based on diffractive deep neural networks (D2NNs) has been introduced to execute a function as the input light diffracts through passive layers, designed by deep learning using a computer. Here we introduce improvements to D2NNs by changing the training loss function and reducing the impact of vanishing gradients in the error back-propagation step. Using five phase-only diffractive layers, we numerically achieved a classification accuracy of 97.18 and 87.67 respectively; using both phase and amplitude modulation (complex-valued) at each layer, our inference performance improved to 97.81 respectively. Furthermore, we report the integration of D2NNs with electronic neural networks to create hybrid classifiers that significantly reduce the number of input pixels into an electronic network using an ultra-compact front-end D2NN with a layer-to-layer distance of a few wavelengths, also reducing the complexity of the successive electronic network. Using a 5-layer phase-only D2NN jointly-optimized with a single fully-connected electronic layer, we achieved a classification accuracy of 98.17 recognition of handwritten digits and fashion products, respectively. Moreover, the input to the electronic network was compressed by >7.8 times down to 10x10 pixels, making the jointly-optimized hybrid system perform classification with a simple electronic layer. Beyond creating low-power and high-frame rate ubiquitous machine learning platforms, such D2NN-based hybrid neural networks will find applications in optical imager and sensor design.
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