As computing resource demands continue to escalate in the face of big data, cloud-connectivity and the internet of things, it has become imperative to develop new low-power, scalable architectures. Neuromorphic photonics, or photonic neural networks, have become a feasible solution for the physical implementation of efficient algorithms directly on-chip. This application is primarily due to the linear nature of light and the scalability of silicon photonics, specifically leveraging the wide-scale complementary metal-oxide-semiconductor (CMOS) manufacturing infrastructure used to fabricate microelectronics chips. Current neuromorphic photonic implementations stem from two paradigms: wavelength coherent and incoherent. Here, we introduce a novel architecture that supports coherent and incoherent operation to increase the capability and capacity of photonic neural networks with a dramatic reduction in footprint compared to previous demonstrations. As a proof-of-principle, we experimentally demonstrate simple addition and subtraction operations on a foundry-fabricated silicon photonic chip. Additionally, we experimentally validate an on-chip network to predict the logical 2-bit gates AND, OR, and XOR to accuracies of 96.8%, 99%, and 98.5%, respectively. This architecture is compatible with highly wavelength parallel sources, enabling massively scalable photonic neural networks.

We present a new architecture for performing learning-based tasks directly on an integrated photonic chip. We experimentally realize logical 2-bit gates AND, OR, and XOR to accuracies of 96.8%, 99%, and 98.5%, respectively.