Optical Diffractive Neural Networks (ODNNs) have emerged as a new class of AI systems that hold promise for fast and low energy classification of scenes. While these systems resemble electronic neural networks, they also have important differences because they need to satisfy constraints imposed by physical laws of light propagation and light-matter interactions. This brings a number of interesting fundamental questions regarding the ultimate performance that can be achieved, the optimal structure of materials, and even how effectively they can be trained. In this presentation, we will present our efforts to address these questions. In particular, we will discuss how co-design of the diffractive material, the system architecture, and the training algorithms is essential to achieve the best performance and also reveal underlying properties. For example, universal scaling of the performance emerges which differs from traditional electronic NNs. We will also discuss how the properties of the systems differs for coherent and incoherent light. Finally, the role of depth will also be addressed.
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