April 17, 2025 – 10:30 am – Wu and Chen Auditorium
ASML/Cymer Chair of Advanced Optical Technologies and Distinguished Professor in Electrical and Computer Engineering (ECE) at the University of California, San Diego (UCSD)
“Foundry Enabled Chip-Scale Photonics Technology and Applications”
Abstract”: Dense photonic integration requires miniaturization of materials, devices, circuits and systems, including passive components (e.g., engineered composite metamaterials, filters, etc.), active components (e.g., modulators and nonlinear wave mixers) and integrated circuits (Fourier transform spectrometer, programmable phase modulator of free space modes, linear algebra processors, etc.). In this talk we will discuss recent progress in developing CMOS compatible nonlinear optical materials as well as examples of foundry enabled silicon photonic circuits and systems. Specifically, we will review silicon photonics-based Fourier transform spectrometer (Si-FTS) that can bring broadband operation and fine resolution to the chip scale. Here we will present the modeling and experimental demonstration of a thermally tuned Si-FTS accounting for dispersion, thermo-optic non-linearity, and thermal expansion. We show how these effects modify the relation between the spectrum and interferogram of a light source and we develop a quantitative correction procedure through calibration with a tunable laser. Providing design flexibility and robustness, the Si-FTS is poised to become a fundamental building block for on-chip spectroscopy. Moreover, taking advantage of nanofabrication we will discuss on-chip spectrometers using stratified waveguide filters and machine learning. Moving forward, we will discuss chip-scale integrated circuit/system that will allow to realize linear algebra accelerators with superior performance in speed, energy consumption and size compared to its electronic counterpart. Such a system can be manufactured using monolithic CMOS process and impact such applications as 5G/6G and beyond wireless MIMO systems as well as deep learning and artificial intelligence.