The Next Frontier In Silicon Photonic Design: Experimentally Validated Statistical Models
Geoff Duggan1, James Pond1, Xu Wang1, Ellen Schelew1, Federico Gomez1, Milad Mahpeykar1, Ray Chung1, Zequin Lu1, Parya Samadian1, Jens Niegemann1, Adam Reid1, Roberto Armenta1, Dylan McGuire1, Peng Sun2, Jared Hulme2, Mudit Jan2 and Ashkan Seyedi2
1Lumerical, US
2HPE, US
ABSTRACT
Silicon photonics has made tremendous progress in recent years and is now a critical technology embedded in many commercial products, particularly for data communications, while new products in sensing, AI and even quantum information technologies are in development. High quality processes from multiple foundries, supported by sophisticated electronic-photonic design automation (EPDA) workflows have made these advancements possible. Although several initiatives have begun to address the issue of manufacturing variability in photonics, these approaches have not been integrated meaningfully into EPDA workflows which lag well behind electronic integrated circuit workflows. Contributing to this deficiency has been a lack of data to calibrate statistical photonic compact models used in photonic circuit and system simulation. We present our current work in developing tools to calibrate.statistical photonic compact models and compare our results against experimental data.
