An Electro-Optic Lithium Niobate on Insulator (LNOI) Multimode Interference Accelerator for Reconfigurable Optical Neural Networks

To overcome the thermal loss and interconnect delays of conventional electronic processors under escalating AI computing workloads, analog optical processors have emerged as a compelling low-latency alternative. This work introduces an integrated optical neural accelerator leveraging an electro-optically reconfigurable multimode interference (MMI) framework on a lithium-niobate-on-insulator (LNOI) platform. By harnessing localized spatial mode interference through micro-electrodes strategically placed within the MMI region, the device performs trainable matrix operations with a minimal on-chip insertion loss of 1.42 dB. A hardware-in-the-loop in situ (HIL in situ) calibration framework employing the Nelder–Mead optimization algorithm is developed to adaptively adjust local refractive indices and compensate for physical hardware imperfections. The system’s performance is validated through numerical electromagnetic simulations applied to the famous Iris flower classification benchmark, demonstrating the self-learning capabilities of the integrated optical neural network with an accuracy of 72%. These findings present a compact, low-loss building block for next-generation photonic tensor processors.