Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks

TL;DR

This paper enhances Diffractive Deep Neural Networks (D2NNs) by improving training methods, demonstrating high accuracy in optical classification tasks, and integrating them with electronic neural networks to create efficient hybrid classifiers with significant input compression.

Contribution

The paper introduces training improvements for D2NNs, achieves high classification accuracy, and demonstrates their integration with electronic neural networks for compact, efficient hybrid optical-electronic classifiers.

Findings

Achieved 97.18% accuracy with phase-only D2NNs on handwritten digits.

Improved accuracy to 97.81% using complex-valued modulation.

Successfully integrated D2NNs with electronic networks, reducing input size by over 7.8 times.

Abstract

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 89.13% for optical recognition of handwritten digits and fashion products, respectively; using both phase and amplitude modulation (complex-valued) at each layer, our inference performance improved to 97.81% and 89.32%, 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 98.71% and 90.04% for the 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. Beyond creating low-power and high-frame rate machine learning platforms, D2NN-based hybrid neural networks will find applications in smart optical imager and sensor design.