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Reconfigurable optical synaptic weighting engine using a liquid crystal-based multimode interference coupler
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DOI：10.3389/fnano.2025.1593347.png)
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Abstract
En 中文
Artificial neural networks (ANNs) have become ubiquitous in high-performance information processing. However; conventional electronic hardware; based on the sequential Von Neumann architecture; struggles to efficiently support ANN computations due to their inherently massive parallelism. Additionally; electrical parasitics further limit energy efficiency and processing speed; pushing traditional architectures toward their fundamental constraints. To overcome these limitations; researchers are exploring integrated photonics; leveraging the inherent parallelism of optical devices for more efficient computation. Despite these efforts; most existing optical computing schemes encounter scalability challenges; given that the number of optical elements typically grows quadratically with the computational matrix size. In this work; a compact programmable multimode interference (MMI) coupler on an indium phosphide membrane platform is proposed for realizing a photonic feedforward neural network. MMIs present a unique opportunity to accelerate matrix multiplication processes by exploiting the interference properties of light modes; promising advancements in both speed and energy efficiency. The programmable MMI coupler; comprising four input and three output (4 × 3 MMI) InP waveguides; makes use of hybrid integration of liquid crystals as cladding material; which offers reconfigurability to the MMI structure. Three electrically tunable sections are made to perform parallel multiplication operations. A novel modeling technique is introduced to facilitate effective training and inference operations. Finite-Difference Time-Domain (FDTD) simulations are employed for calculating the optical mode propagation process within the programmable MMI structure. Based on the FDTD results; a compact optical neural network is implemented and assessed on the Iris flower dataset; demonstrating a testing accuracy of 86.67%. This novel MMI device concept offers a promising pathway toward energy-efficient; scalable optical computing systems; contributing to the advancement of next-generation artificial intelligence hardware.
Keywords:
artificial neural networks
photonic computing
multimode interference coupler
optical neural network
integrated photonics
Journal
F
IF:
3.8
Papers:
508
Citations:
1.3K