Numerical Analysis of High-Performance Binary-to-Gray Converter Using Photonic Crystal Waveguides for Neural Network Hardware Acceleration

This paper presents an all-optical two-bit binary-to-gray code converter based on linear photonic crystal (PhC) waveguides aimed at improving efficiency and integration within binary neural network hardware accelerators. The converter leverages interference effects within 2D photonic crystal waveguides to achieve reliable binary-to-gray conversion with minimal power requirements and a compact design footprint. Simulation results exhibit exceptional extinction ratios of 9.49 dB and 10.11 dB for output bits G₀ and G₁, respectively, within a 10 μm × 10 μm footprint. Additionally, the device achieves a delay of 0.52 μs, representing a significant reduction in delay and energy consumption compared to conventional approaches. The proposed converter is compatible with photonic integrated circuits and offers dual functionality, supporting reversible Feynman logic for signal duplication and integration within binary neural networks. This compact, low-power converter marks a step forward in developing high-efficiency optical components for telecommunications, data processing, and neuromorphic computing applications.