Cascadable all-optical NAND gates using diffractive networks

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.

24 Pages, 5 Figures

References in corpus (10)

• 11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks
• Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
• Scalable Optical Learning Operator
• Terahertz Pulse Shaping Using Diffractive Surfaces
• Computational Imaging Without a Computer: Seeing Through Random Diffusers at the Speed of Light
• All-Optical Information Processing Capacity of Diffractive Surfaces
• Misalignment Resilient Diffractive Optical Networks
• Scale-, shift- and rotation-invariant diffractive optical networks
• Computer-free, all-optical reconstruction of holograms using diffractive networks
• All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces

Cited by in corpus (9)

• Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
• Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network
• All-optical image classification through unknown random diffusers using a single-pixel diffractive network
• All-optical complex field imaging using diffractive processors
• Time-lapse image classification using a diffractive neural network
• Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network
• Programmable metasurfaces for future photonic artificial intelligence
• Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers
• Coherence Awareness in Diffractive Neural Networks