activity
20232026
most citedReinforcement Learning for Photonic Component Design

19 citations · 30 across the 6 of their papers we have counts for

collaborators

6 papers

quant-ph2026

Resolving and resetting the charge environment of single T-centers in silicon p-i-n waveguides

Chaoshen Zhang, Hanbin Song, Lukasz Komza +6

The silicon T-center is a telecom-band spin-photon interface in a manufacturable photonics platform. In nanophotonic devices, however, its optical linewidth is broadened by a fluct…

physics.optics2025★ 6 cited

Milliwatt-level UV generation using sidewall poled lithium niobate

C. A. A. Franken, S. S. Ghosh, C. C. Rodrigues +8

Integrated coherent sources of ultra-violet (UV) light are essential for a wide range of applications, from ion-based quantum computing and optical clocks to gas sensing and micros…

physics.optics2024★ 3 cited

GHz-rate optical phase shift in light matter interaction-engineered, silicon-ferroelectric nematic liquid crystals

Iman Taghavi, Omid Esmaeeli, Sheri Jahan Chowdhury +10

Organic electro-optic materials have demonstrated promising performance in developing electro-optic phase shifters. Their integration with other silicon photonic processes, nanofab…

physics.optics2024★ 1 cited

Packaged Cryogenic Photon Pair Source Using an Effective Packaging Methodology for Cryogenic Integrated Optics

Donald Witt, Lukas Chrostowski, Jeff Young

A new cryogenic packaging methodology that is widely applicable to packaging any integrated photonics circuit for operation at both room temperature and cryogenic temperature is re…

physics.optics2023★ 1 cited

Cryogenic Optical Packaging Using Photonic Wire Bonds

Becky Lin, Donald Witt, Jeff F. Young +1

We present the required techniques for the successful low loss packaging of integrated photonic devices capable of operating down to 970 mK utilizing photonic wire bonds. This scal…

physics.optics2023★ 19 cited

Reinforcement Learning for Photonic Component Design

Donald Witt, Jeff Young, Lukas Chrostowski

We present a new fab-in-the-loop reinforcement learning algorithm for the design of nano-photonic components that accounts for the imperfections present in nanofabrication processe…