Prof. David Z. Pan and his research group won the Best Paper award from the ACM International Symposium on Physical Design (ISPD) 2020 for their paper “TEMPO: Fast Mask Topography Effect Modeling with Deep Learning.” Authors on the paper were Wei Ye, Mohamed Baker Alawieh, Yuki Watanabe, Shigeki Nojima, Yibo Lin, and David Z. Pan.

“With a second wave of the coronavirus likely to appear right as we get into flu season, there’s an urgent need for diagnostics that can differentiate between COVID-19 and influenza,” said Deji Akinwande, a professor in the Cockrell School of Engineering’s Department of Electrical and Computer Engineering.

The team’s findings were published in May in Nature Photonics. The team includes UT’s Seth R. Bank, a professor in the Cockrell School’s Department of Electrical and Computing Engineering, UT electrical and computer engineering Ph.D. student Stephen March, and, from UVA, Campbell and postdoc Andrew Jones.

Prof Alan Bovik, Prof. Sriram Vishwanath and their students have been awarded 2020 EURASIP Best Paper Awards.

Prof. Deji Akinwande and his research team published their findings today in the journal Nature Electronics.

The discovery is part of a larger $6.5 million project sponsored by the U.S. Air Force Office of Scientific Research and led by electrical and computer engineering professors Ray Chen and David Z. Pan.

Texas ECE held a Virtual Honors event on Wednesday, May 20 to honor students and faculty for their successes and contributions during the 2019-2020 academic year.

Texas ECE is on the front lines of the global response to the coronavirus pandemic. Our researchers are racing to develop innovations to support the fight against COVID-19.

Two student teams in Prof. Jaydeep Kulkarni’s 382M.8 VLSI-II course won the inaugural Intel Outstanding ASIC Design Contest for their excellent performance in the final class project.  A team of experts headed by Dr. Prashant Joshi from Intel Austin Design Center served as judges for this design contest.

They plan to develop novel BEOL transistor technologies to realize low power, high density, 3-D stacked SRAM technologies which can potentially address energy efficient data storage needs of next generation Integrated Circuits.