近日，美国加州大学圣巴巴拉分校的Paolo Pintus及其研究小组与美国匹兹堡大学的Nathan Youngblood等人合作并取得一项新进展。经过不懈努力，他们提出用于光子内存计算的具有超高续航时间的集成非互易磁光学方法。相关研究成果已于2024年10月23日在国际知名学术期刊《自然—光子学》上发表。

该研究团队提出一种新的方法，使用包含异质集成的硅微环谐振器上铈掺杂钇铁石榴石（Ce:YIG）的磁光存储单元，为内存光子计算编码光学权重。研究表明，利用此类磁光材料中的非互易相移比现有架构具有多个关键优势，为片上光学处理提供了一个快速（1纳秒）、高效（每比特143飞焦）且稳健（24亿次编程周期）的平台。

据悉，与现有的用于人工智能和机器学习等新兴应用的数字硬件相比，在光学领域处理信息有望在速度和能效方面带来优势。光子处理的一种典型方法是将快速变化的光学输入向量与固定光学权重的矩阵相乘。然而，目前使用光子存储单元阵列在芯片上编码这些权重受到了一系列材料和器件层面问题的限制，包括编程速度、消光比和耐久性等等。

附：英文原文

Title: Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing

Author: Pintus, Paolo, Dumont, Mario, Shah, Vivswan, Murai, Toshiya, Shoji, Yuya, Huang, Duanni, Moody, Galan, Bowers, John E., Youngblood, Nathan

Issue&Volume: 2024-10-23

Abstract: 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 (1ns), efficient (143fJ per bit) and robust (2.4 billion programming cycles) platform for on-chip optical processing.

DOI: 10.1038/s41566-024-01549-1

Source: https://www.nature.com/articles/s41566-024-01549-1