A fully programmable two-dimensional photonic chip for machine learning

On-chip photonic-neural-network processors have potential benefits in both speed and energy efficiency but have not yet reached the scale to compete with electronic processors. The dominant paradigm is to build integrated-photonic processors using relatively bulky discrete components connected by single-mode waveguides. A far more compact alternative is to avoid explicitly defining any components and instead sculpt the continuous substrate of the photonic processor to directly perform the computation using waves freely propagating in two dimensions. In this talk, I will present our recent work [1] on experimentally realizing this approach with a device whose refractive index as a function of space, n(x,z), can be rapidly reprogrammed. This device combines photoconductive gain with the electro-optic effect in a lithium niobate slab waveguide. Using this device, we performed neural-network inference with up to 49-dimensional input vectors in a single pass. [1]: T. Onodera*, M.M. Stein*, et al. arXiv:2402.17750 (2024)