Compact diffractive optical neural networks with programmable binary nonlinearity and physics-aware adaptive readout
Zengguang Liu, Jinqi Tan, Guangbo Zhang, Tian-Fei Zhao, Yuankai Guo, can zhao, Zhanpeng Zhang, Liwei Liu · Photonics Research · 2026
Diffractive optical neural networks (DONNs) promise ultra-high-speed computing but face persistent challenges in realizing optical nonlinearity and maintaining experimental robustness. We demonstrate the c-DONN, a compact architecture utilizing a digital micromirror device (DMD), to implement trainable binary nonlinearity. Furthermore, we propose a physics-aware adaptive readout mechanism that significantly mitigates alignment errors and systemic noise without requiring time-consuming in situ retraining. Our prototype system achieves a classification accuracy of over 95% on the MNIST dataset and exceeds 92% in fingerprint recognition tasks. By addressing critical hurdles in nonlinearity and robustness, this work establishes a practical engineering framework for next-generation photonic intelligent systems in biometric and edge computing applications.