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Adv."],"published-print":{"date-parts":[[2026,9,18]]},"abstract":"<jats:p>Electromagnetic diffractive neural networks (DNNs) enable ultra\u2013low-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. 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