  The finite-difference time-domain (FDTD) method, which is important in
photonic hardware design flow, is widely adopted to solve time-domain Maxwell
equations. However, FDTD is known for its prohibitive runtime cost, taking
minutes to hours to simulate a single device. Recently, AI has been applied to
realize orders-of-magnitude speedup in partial differential equation (PDE)
solving. However, AI-based FDTD solvers for photonic devices have not been
clearly formulated. Directly applying off-the-shelf models to predict the
optical field dynamics shows unsatisfying fidelity and efficiency since the
model primitives are agnostic to the unique physical properties of Maxwell
equations and lack algorithmic customization. In this work, we thoroughly
investigate the synergy between neural operator designs and the physical
property of Maxwell equations and introduce a physics-inspired AI-based FDTD
prediction framework PIC2O-Sim which features a causality-aware dynamic
convolutional neural operator as its backbone model that honors the space-time
causality constraints via careful receptive field configuration and explicitly
captures the permittivity-dependent light propagation behavior via an efficient
dynamic convolution operator. Meanwhile, we explore the trade-offs among
prediction scalability, fidelity, and efficiency via a multi-stage partitioned
time-bundling technique in autoregressive prediction. Multiple key techniques
have been introduced to mitigate iterative error accumulation while maintaining
efficiency advantages during autoregressive field prediction. Extensive
evaluations on three challenging photonic device simulation tasks have shown
the superiority of our PIC2O-Sim method, showing 51.2% lower roll-out
prediction error, 23.5 times fewer parameters than state-of-the-art neural
operators, providing 300-600x higher simulation speed than an open-source FDTD
numerical solver.
