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

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.Researchers demonstrate optical weights for in-memory photonic computing using magneto-optic memory cells comprising Ce:YIG on silicon micro-ring resonators. Non-reciprocal phase shift provides a fast, efficient and robust integrated optical processing platform.