“I want to thank Synopsys for providing a convenient and efficient integrated photonic design and layout tool. “We devised a hardware-efficient butterfly-style photonic-electronic neural chip to implement deep learning tasks,” said Feng. The team provided the following comments: MSF surface errors are a fascinating and important research area, and I hope that the tools that we have developed within CODE V continue to enable exploration and deeper understanding of this topic.” “In addition, I would like to thank my advisor, Professor Thomas Suleski, and mentors from the Center for Freeform Optics for their guidance and support. “I want to thank Synopsys for this award and for the educational license and technical support they have provided,” said DeMars. DeMars’ research is part of an ongoing project with the Center for Freeform Optics, which partners with universities and industry organizations to advance freeform optical technologies. DeMars received an award for the project titled, “Optical modeling of generalized mid-spatial-frequency surface errors.” DeMars used CODE V to model and predict the performance of optical systems containing residual mid-spatial frequency (MSF) surface errors from sub-aperture manufacturing. Luke DeMars is a Ph.D candidate in optical engineering at the University of North Carolina at Charlotte.
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