Processing information in the optical domain promises advantages in both speed and energy efficiency over existing digital hardware for a variety of emerging applications in artificial intelligence and machine learning. A typical approach to photonic processing is to multiply a rapidly changing optical input vector with a matrix of fixed optical weights. However, encoding these weights on-chip using an array of photonic memory cells is currently limited by a wide range of material- and device-level issues, such as the programming speed, extinction ratio and endurance, among others. Here we propose a new approach to encoding optical weights for in-memory photonic computing using magneto-optic memory cells comprising heterogeneously integrated cerium-substituted yttrium iron garnet (Ce:YIG) on silicon micro-ring resonators. We show that leveraging the non-reciprocal phase shift in such magneto-optic materials offers several key advantages over existing architectures, providing a fast (1 ns), efficient (143 fJ per bit) and robust (2.4 billion programming cycles) platform for on-chip optical processing.

与现有的数字硬件相比，在光学域中处理信息有望在速度和能效方面具有优势，适用于人工智能和机器学习中的各种新兴应用。光子处理的一种典型方法是将快速变化的光输入向量与固定光权重矩阵相乘。然而，使用一系列光子存储单元在片上编码这些权重目前受到各种材料和器件级问题的限制，例如编程速度、消光比和耐久性等。在这里，我们提出了一种使用磁光存储单元编码光学权重的新方法，该方法使用磁光存储单元，该单元由硅微环谐振器上的异构集成铈取代钇铁石榴石 （Ce：YIG） 组成。我们表明，与现有架构相比，利用这种磁光材料中的非互易相移提供了几个关键优势，为片上光学处理提供了一个快速（1 ns）、高效（每比特 143 fJ）和稳健（24 亿个编程周期）平台。