Optical neural networks have emerged as a promising avenue for implementing artificial intelligence applications, with matrix computations being a crucial component. However, the existing implementations based on microring resonators (MRRs) face bottlenecks in integration, power efficiency, and scalability, hindering the practical applications of wavelength division multiplexing (WDM)-based matrix-vector multiplications at the hardware level. Here we present a photonic crystal nanobeam cavity (PCNC) matrix core. Remarkably compact with dimensions reduced to 20µm×0.5µm${20}\;{\unicode{x00B5}{\rm m}} \times {0.5}\;{\unicode{x00B5}{\rm m}}$, the PCNC unit exhibits a thermal tuning efficiency more than three times that of MRRs. Crucially, it is immune to the free spectral range constraint, thus able to harness the wealth of independent wavelength channels provided by WDM. A 3×3${3} \times {3}$ PCNC core chip is demonstrated for animal face recognition and a six-channel chip is employed for handwritten digit classification to demonstrate the scalability. The PCNC solution holds immense promise, offering a versatile platform for next-generation photonic artificial intelligence chips.

中文翻译：

---

紧凑、高效且可扩展的纳米束核心，用于光子矩阵矢量乘法

光神经网络已成为实现人工智能应用的一个有前景的途径，其中矩阵计算是一个关键组成部分。然而，基于微环谐振器（MRR）的现有实现在集成、功率效率和可扩展性方面面临瓶颈，阻碍了基于波分复用（WDM）的矩阵矢量乘法在硬件层面的实际应用。在这里，我们提出了光子晶体纳米束腔（PCNC）矩阵核心。非常紧凑，尺寸减少至20微米× 0.5 _µ m${20}\;{\unicode{x00B5}{\rm m}} \times {0.5}\;{\unicode{x00B5}{\rm m}}$，PCNC 单元的热调谐效率是 MRR 的三倍以上。至关重要的是，它不受自由光谱范围限制，因此能够利用 WDM 提供的丰富的独立波长通道。演示了用于动物人脸识别的3 × 3${3} \times {3}$ PCNC 核心芯片PCNC 解决方案前景广阔，为下一代光子人工智能芯片提供多功能平台。