Cascadable all-optical NAND gates using diffractive networks
This work addresses the need for scalable, low-latency, and power-efficient optical computing components, though it is incremental as it builds on existing diffractive network methods.
The paper tackled the problem of designing a core unit for all-optical processors by presenting cascadable all-optical NAND gates using diffractive neural networks, achieving the ability to perform complex logical functions like AND, OR, and a half-adder through numerical optimization of a 4-layer passive design.
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.