MRR‐Assisted MZI Crossbar Array for Energy‐Efficient Optical Parallel Computing

Abstract Optical neural networks (ONNs) have emerged as a photonic platform for accelerating artificial intelligence workloads, relying on optical matrix operations as their computational basis. Yet existing implementations face critical challenges in precision, cascaded optical loss, and inefficient weight‐programming mechanisms, which limit scalability and practical performance. A micro‐ring resonator (MRR)‐assisted Mach–Zehnder interferometer (MZI) crossbar architecture (MMCA) that addresses these limitations through parallelized optical computing is presented. By replacing conventional waveguide‐based coupling with MRR‐enabled wavelength‐selective routing, the design suppresses the multiplicative optical losses inherent in cascaded combiner networks. Concurrently, MZI‐based crossbar nodes enable deterministic one‐to‐one weight mapping with enhanced speed and robustness. The architecture synergizes structural merits from mainstream optical computing paradigms while overcoming critical bottlenecks. Experimental validation demonstrates 6‐bit weight‐loading precision and high energy efficiency. Integrated wavelength‐division multiplexing further enables parallel computation, achieving 95.1% classification accuracy on convolutional neural network benchmarks. This approach establishes a scalable framework for low‐loss, high‐precision optical matrix operations, advancing the development of energy‐efficient photonic AI accelerators.