F. Ashtiani, M. H. Idjadi, T. C. Hu, S. Grillanda, D. Neilson, M. Earnshaw, M. Cappuzzo, R. Kopf, A. Tate, A. Blanco-Redondo
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A surface-normal photodetector as nonlinear activation function in diffractive optical neural networks
Optical neural networks (ONNs) enable high speed, parallel, and energy efficient processing compared to their conventional digital electronic counterparts. However, realizing large scale ONN 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 an optical-in–electrical-out (O–E) nonlinear response to replace the camera sensor that enables about three orders of magnitude faster (5.7 µs 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 O–E nonlinear activation function for diffractive ONNs. To show the applicability of the proposed neural nonlinearity, successful classification simulations of the MNIST and Fashion MNIST datasets using the measured response of SNPD with accuracy comparable to that of an ideal ReLU function are demonstrated.
APL PhotonicsPhysics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
10.30
自引率
3.60%
发文量
107
审稿时长
19 weeks
期刊介绍:
APL Photonics is the new dedicated home for open access multidisciplinary research from and for the photonics community. The journal publishes fundamental and applied results that significantly advance the knowledge in photonics across physics, chemistry, biology and materials science.