Thomas Van Vaerenbergh received the master's degree in applied physics and the Ph.D. degree in photonics from Ghent University, Ghent, Belgium, in 2010 and 2014, respectively. He was awarded the scientific prize Alcatel-Lucent Bell/FWO for his PhD thesis on all-optical spiking neurons in silicon photonics. In 2014, he joined the Palo Alto-based division of the Large-Scale Integrated Photonics team in Hewlett Packard Labs, part of Hewlett Packard Enterprise (HPE). Since 2019, he has been incubating a research team in HPE Belgium, expanding HPE’s research activities related to photonics and AI in the EMEA region. His main research interests include optical computing, analog photonic and electronic accelerators for combinatorial optimization, the modeling and design of passive silicon photonic devices, such as microring resonators and grating couplers, and the usage of state-of-the-art machine learning techniques to facilitate the design of photonic devices, circuits and devices.
Over the past decade, more automation has trickled into the workflow of designers of photonic devices. Specifically, inverse design techniques based on the adjoint method have allowed designers to find low-loss passive integrated devices based on local search techniques. Lately, to avoid the limitations of inherently local search techniques, which might get stuck in local minima if sub-optimal initial conditions are provided, the field has explored global optimization techniques, inspired by recent progress in physics-informed neural networks and related trends in the machine learning community. In this talk, we will contrast and compare the latest progress in both local and global inverse design techniques, with a particular focus on their performance for integrated photonic devices and circuits, as well as ease-of-use.