Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing

Inverse-designed nanophotonic devices offer promising solutions for analog optical computation, where high-density photonic integration is critical for scaling computational complexity. Here, we present an inverse-designed photonic neural network (PNN) accelerator, enabling ultra-compact and energy-efficient optical computing. Using a wave-based inverse-design method based on three-dimensional finite-difference time-domain simulations, we exploit the linearity of Maxwell’s equations to reconstruct arbitrary spatial fields through optical coherence. Each subwavelength voxel serves as a trainable degree of freedom, yielding a computational density of approximately 400 million parameters per mm². By decoupling the forward-pass process into linearly separable simulations, our approach is highly amenable to computational parallelism. We experimentally demonstrate two inverse-designed PNN accelerators, achieving on-chip MNIST and MedNIST classification accuracies of 89% and 90% respectively, within footprints of 20 × 20 µm² and 30 × 20 µm². Our results establish a scalable, energy-efficient platform for photonic computing, bridging inverse nanophotonic design with high-performance optical information processing. Scaling of optical computing requires computationally dense and efficient hardware. Here, the authors inverse-design and experimentally demonstrate ultracompact nanophotonic neural network accelerators with high computational density, enabling scalable and energy-efficient analog photonic computing.