The prediction and design of photonic features have traditionally been guided by theory‐driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data‐driven methods for photonics. Here, we demonstrate several predictive and generative data‐driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two‐dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high‐accuracy convolutional neural network for band structure prediction, with orders‐of‐magnitude speedup compared to conventional theory‐driven solvers. Separately, we demonstrate an approach to high‐throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse‐magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data‐driven tools in photonics and the natural sciences.

The field of side-channel analysis has made significant progress over time. Side-channel analysis is now used in practice in design companies as well as in test laboratories, and the security of products against side-channel attacks has significantly improved. However, there are still some remaining issues to be solved for side-channel analysis to become more effective. Side-channel analysis consists of two steps, commonly referred to as identification and exploitation. The identification consists of understanding the leakage and building suitable models. The exploitation consists of using the identified leakage models to extract the secret key. In scenarios where the model is poorly known, it can be approximated in a profiling phase. There, machine learning techniques are gaining value. In this paper, we conduct extensive analysis of several machine learning techniques, showing the importance of proper parameter tuning and training. In contrast to what is perceived as common knowledge in unrestricted scenarios, we show that some machine learning techniques can significantly outperform template attacks when properly used. We therefore stress that the traditional worst case security assessment of cryptographic implementations, that mainly includes template attacks, might not be accurate enough. Besides that, we present a new measure called the Data Confusion Factor that can be used to assess how well machine learning techniques will perform on a certain dataset.