Microwave diffractive neural network chips for sensing and computing

Abstract

Electromagnetic diffractive neural networks (DNNs) enable ultra–low-power, low-latency artificial intelligence (AI) inference, yet optical implementations suffer from fabrication and scalability limits, and metasurface microwave systems remain bulky. We present a chip-scale microwave diffractive neural network (MDNN) fabricated in a GaAs semiconductor process, integrating cascaded couplers and phase shifters to implement a diffraction network within a millimeter-scale footprint. The MDNN chip reduces the size of conventional MDNNs by over four orders of magnitude, achieves a computational latency of 2.05 ns, and delivers a system-level energy efficiency of 0.83 TOPS/W. We demonstrate its versatility through three functional prototypes: MNIST handwritten digit recognition, multi-user interference suppression, and real-time obstacle perception for drones. These experiments achieved more than 86% accuracy, validating the capability of the MDNN chip to directly perform both digital image processing and in-situ electromagnetic information processing in the microwave domain. We hope this chip architecture opens a new pathway toward highly integrated designs for electromagnetic DNNs.