Class-specific differential detection in diffractive optical neural networks (Conference Presentation)

Abstract

We introduce a differential measurement scheme in diffractive neural networks, where object-classes are assigned to separate opto-electronic detector pairs and the class-inference is made based on the maximum normalized differential signal. This scheme enables diffractive networks to achieve blind-testing accuracies of 98.54% and 48.51% for MNIST and CIFAR-10 datasets, respectively. These accuracies improve to 98.52% and 50.82%, when differential detection is combined with the joint-training of parallel diffractive networks, with each specializing on a separate object-class. Finally, we report independently-trained diffractive networks that project their output-light onto a common plane to achieve 98.59% and 51.44%, for the same datasets, respectively.