I-Corps Teams: Machine Learning (ML)-powered Data Analyzer for Radio Frequency Integrated Circuits (RFIC) Design

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The broader impact/commercial potential of this I-Corps project is the development of a software platform for radio frequency integrated circuits (RFIC) design with more accurate simulation prediction. The RFIC industry, key for wireless systems, is approximately $50 B, and RFIC companies spend about $15 B on the final trial-and-error fabrication process known as "tape-out". Each tape-out takes 12 weeks and costs ~$1 M, and 3-5 may be required. Significant savings may be realized in both time and cost by reducing design iterations.This I-Corps project is based on the development of RFICs . RFIC’s parasitic effect and poor simulation accuracy requires multiple tape-outs to meet specifications. In the future 5G era, this problem gets worse as mmWave frequency parasitics are even harder to model and more tape-out rounds will be required. Using the technology under development, it may be possible to improve significantly simulation accuracy by combining traditional physics-based models and customized machine-learning (ML) training in RFIC simulation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.