Discrete global optimization algorithms for the inverse design of silicon photonics devices

The photonics community counts on inverse design approaches to provide new designs of miniaturized photonic components. Although the structures are composed of two materials only, the dominant method used to tackle such a problem is to make the problem continuous through a relaxation of the binary constraint and to use a gradient-based approach. Global optimization algorithms working on continuous problems actually often fail to produce satisfying solutions in such a context characterized by a large number of parameters – especially when space is discretized into a large number of pixels or voxels. However, we show here for three different photonics problems that global discrete optimization algorithms, which are adapted to this kind of problems, can provide efficient solutions which are relatively regular, physically understandable and close to being buildable. Such algorithms constitute overlooked but valuable tools for inverse design problems. Our preferred method is using a starting point provided by a gradient based algorithm, and run an optimization using Lengler’s algorithm equipped with a simple smoothing operator to make the algorithm aware of the physical nature of the problem.