Cascadable all-optical NAND gates using diffractive networks

TL;DR

This paper presents the design and numerical validation of cascadable all-optical NAND gates using diffractive neural networks, enabling complex optical logical operations with potential for scalable, low-latency optical computing.

Contribution

The authors introduce a novel diffractive neural network architecture for all-optical NAND gates that can be cascaded to perform complex logical functions.

Findings

Successfully designed a 4-layer diffractive neural network for NAND operation.

Demonstrated cascaded optical logic functions including AND, OR, and half-adder.

Validated the cascadability of the optical NAND gates through numerical simulations.

Abstract

Owing to its potential advantages such as scalability, low latency and power efficiency, optical computing has seen rapid advances over the last decades. A core unit of a potential all-optical processor would be the NAND gate, which can be cascaded to perform an arbitrary logical operation. Here, we present the design and analysis of cascadable all-optical NAND gates using diffractive neural networks. We encoded the logical values at the input and output planes of a diffractive NAND gate using the relative optical power of two spatially-separated apertures. Based on this architecture, we numerically optimized the design of a diffractive neural network composed of 4 passive layers to all-optically perform NAND operation using the diffraction of light, and cascaded these diffractive NAND gates to perform complex logical functions by successively feeding the output of one diffractive NAND gate into another. We demonstrated the cascadability of our diffractive NAND gates by using identical diffractive designs to all-optically perform AND and OR operations, as well as a half-adder. Cascadable all-optical NAND gates composed of spatially-engineered passive diffractive layers can serve as a core component of various optical computing platforms.