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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月4日

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  • Joel Sved,
  • Shijie Song,
  • Liwei Li,
  • George Li,
  • Debin Meng,
  • Xiaoke Yi

摘要

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.