Massively scalable wavelength diverse integrated photonic linear neuron. Issue 3 (1st September 2022)

• Record Type: Journal Article
• Authors: van Niekerk, Matthew; Rizzo, Anthony; Rubio, Hector; Leake, Gerald; Coleman, Daniel; Tison, Christopher; Fanto, Michael; Bergman, Keren; Preble, Stefan
• Is Part Of: Neuromorphic computing and engineering. Volume 2:Issue 3(2022)
• Publication Date: 2022-09-01
• Subjects: massively -- scalable -- integrated -- photonics -- neuromorphic; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/ac8ecc
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: As computing resource demands continue to escalate in the face of big data, cloud-connectivity and the internet of things, it has become imperative to develop new low-power, scalable architectures. Neuromorphic photonics, or photonic neural networks, have become a feasible solution for the physical implementation of efficient algorithms directly on-chip. This application is primarily due to the linear nature of light and the scalability of silicon photonics, specifically leveraging the wide-scale complementary metal-oxide-semiconductor manufacturing infrastructure used to fabricate microelectronics chips. Current neuromorphic photonic implementations stem from two paradigms: wavelength coherent and incoherent. Here, we introduce a novel architecture that supports coherent and incoherent operation to increase the capability and capacity of photonic neural networks with a dramatic reduction in footprint compared to previous demonstrations. As a proof-of-principle, we experimentally demonstrate simple addition and subtraction operations on a foundry-fabricated silicon photonic chip. Additionally, we experimentally validate an on-chip network to predict the logical 2 bit gates AND, OR, and XOR to accuracies of 96.8%, 99%, and 98.5%, respectively. This architecture is compatible with highly wavelength parallel sources, enabling massively scalable photonic neural networks.