Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning
This work addresses the optimization of electromagnetic devices for photonics researchers, offering incremental improvements in efficiency and performance over existing methods.
The authors tackled the inverse design of photonic grating couplers by developing PHORCED, a technique based on reinforcement learning's policy gradient method, which outperformed local gradient-based methods and achieved faster convergence and >10× fewer simulations in transfer learning scenarios.
We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic Optimization using REINFORCE Criteria for Enhanced Design). This technique uses a probabilistic generative neural network interfaced with an electromagnetic solver to assist in the design of photonic devices, such as grating couplers. We show that PHORCED obtains better performing grating coupler designs than local gradient-based inverse design via the adjoint method, while potentially providing faster convergence over competing state-of-the-art generative methods. As a further example of the benefits of this method, we implement transfer learning with PHORCED, demonstrating that a neural network trained to optimize 8$^\circ$ grating couplers can then be re-trained on grating couplers with alternate scattering angles while requiring >10$\times$ fewer simulations than control cases.