A surface-normal photodetector as nonlinear activation function in diffractive optical neural networks

TL;DR

This paper introduces a novel surface-normal photodetector (SNPD) that acts as a nonlinear activation function in diffractive optical neural networks, significantly improving speed and energy efficiency over traditional camera sensors.

Contribution

The paper presents a new SNPD device with inherent nonlinearity, enabling faster and more energy-efficient nonlinear activation in optical neural networks compared to existing methods.

Findings

SNPD achieves 5.7 microsecond response time.

SNPD consumes less than 10 nanowatts per pixel.

Successful classification of MNIST datasets with accuracy comparable to ideal ReLU.

Abstract

Optical neural networks (ONNs) enable high speed parallel and energy efficient processing compared to conventional digital electronic counterparts. However, realizing large scale systems is an open problem. Among various integrated and non-integrated ONNs, free-space diffractive ONNs benefit from a large number of pixels of spatial light modulators to realize millions of neurons. However, a significant fraction of computation time and energy is consumed by the nonlinear activation function that is typically implemented using a camera sensor. Here, we propose a novel surface-normal photodetector (SNPD) with a nonlinear response to replace the camera sensor that enables about three orders of magnitude faster (5.7 us response time) and more energy efficient (less than 10 nW/pixel) response. Direct efficient vertical optical coupling, polarization insensitivity, inherent nonlinearity with no control electronics, low optical power requirements, and the possibility of implementing large scale arrays make the SNPD a promising nonlinear activation function for diffractive ONNs. To show the applicability, successful classification simulation of MNIST and Fashion MNIST datasets using the measured response of SNPD with accuracy comparable to that of an ideal ReLU function are demonstrated.