Deep neural network (DNN) surrogate models for the accelerated design of optical devices and systems: Moving beyond fully-connected feed forward architectures

Recently, we have proposed the pairing of Deep Neural Networks and evolutionary algorithms as a versatile route for the optimal design of optical devices and systems.¹ Here, we extend that work and investigate the use of deep Convolutional Neural Networks (CNN) as opposed to fully connected feedforward architectures. We show that networks built with convolutional layers, batch normalization (BN), parametric REctified Linear Unit (ReLU) and residual blocks achieve drastic reduction in parameter weights and training epochs in comparison to dense, fully-connected networks for comparable accuracy. The proposed lightweight CNNs when used for approximate objective evaluation in concert with DE global optimization resulted in nearly 8x speedup in convergence time. The choice of a network architecture and optimization of its hyperparameters is a tedious task and it is hoped that the systematic hyperparameter search reported here would assist others in faster model selection.