Deep learning based inverse method for layout design
This addresses layout design challenges in engineering domains like microlithography, but it is incremental as it applies an existing machine learning technique (VAE) to a specific problem.
The authors tackled the problem of layout design with complex constraints by proposing a method using Variational Autoencoders (VAE) to learn constraints and generate compliant designs without imposing constraints during the design stage, and demonstrated its performance on two cases including inverse design of surface diffusion and mask design for optical microlithography.
Layout design with complex constraints is a challenging problem to solve due to the non-uniqueness of the solution and the difficulties in incorporating the constraints into the conventional optimization-based methods. In this paper, we propose a design method based on the recently developed machine learning technique, Variational Autoencoder (VAE). We utilize the learning capability of the VAE to learn the constraints and the generative capability of the VAE to generate design candidates that automatically satisfy all the constraints. As such, no constraints need to be imposed during the design stage. In addition, we show that the VAE network is also capable of learning the underlying physics of the design problem, leading to an efficient design tool that does not need any physical simulation once the network is constructed. We demonstrated the performance of the method on two cases: inverse design of surface diffusion induced morphology change and mask design for optical microlithography.