  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.
