Advancements in semiconductor fabrication over the past decade have catalyzed extensive research into all-optical devices driven by exciton-polariton condensates. Preliminary validations of such devices, including transistors, have shown encouraging results even under ambient conditions. A significant challenge still remains for large scale application however: the lack of a robust solver that can be used to simulate complex nonlinear systems which require an extended period of time to stabilize. Addressing this need, we propose the application of a machine-learning-based Fourier Neural Operator approach to find the solution to the Gross-Pitaevskii equations coupled with extra exciton rate equations. This work marks the first direct application of Neural Operators to an exciton-polariton condensate system. Our findings show that the proposed method can predict final-state solutions to a high degree of accuracy almost 1000 times faster than CUDA-based GPU solvers. Moreover, this paves the way for potential all-optical chip design workflows by integrating experimental data.

翻译：过去十年中，半导体制造技术的进步推动了以激子-极化激元凝聚体驱动的全光器件研究。尽管包括晶体管在内的此类器件的初步验证已在室温条件下展现出令人鼓舞的结果，但其大规模应用仍面临重大挑战：缺乏能够模拟需要长时间才能稳定的复杂非线性系统的鲁棒求解器。针对这一需求，我们提出应用基于机器学习的傅里叶神经算子方法，求解耦合了额外激子速率方程的格罗斯-皮塔耶夫斯基方程。该工作首次将神经算子直接应用于激子-极化激元凝聚体系统。研究结果表明，所提方法能以极高精度预测最终稳态解，其求解速度比基于CUDA的GPU求解器快近1000倍。此外，该方法通过整合实验数据，为全光芯片的设计工作流程铺平了道路。