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An Integrated Data System for Machine Learning based Prediction of Radio Frequency Integrated Circuits (RFIC)

Award Information
Agency: Department of Commerce
Branch: National Institute of Standards and Technology
Contract: 70NANB20H127
Agency Tracking Number: 031-FY20-77
Amount: $106,500.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 9.0
Solicitation Number: 2020-NIST-SBIR-01
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-09-01
Award End Date (Contract End Date): 2021-02-28
Small Business Information
32 Tower Road, Lexington, MA, 02421
DUNS: 117485431
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Qiang Cui
 (336) 541-4257
 qiangcui1984@gmail.com
Business Contact
 Qiang Cui
Phone: (336) 541-4257
Email: qiangcui1984@gmail.com
Research Institution
N/A
Abstract
Radio frequency integrated circuits (RFICs) are key components for wireless communication system. RFIC’s hard-tomodel parasitic effects and poor simulation accuracy require multiple trial-and-error tape-outs (fabrications) to meet product specs. Tape-out is very slow (2 months) and very costly (as high as $2M). In the coming 5G era, this problem gets worse as mmWave frequency parasitic effects are even harder to model, thus more tape-out rounds are needed. HelloMaxwell is a spin-off of MIT research. It combines physics-based models and customized machine learning (ML) to predict RFIC performance. This prediction method can improve RFIC’s prediction accuracy by 97%, which means engineers can reduce the RFIC development time by at least one tape-out round. In order to achieve this improvement, the ML training needs sufficient compatible data across design, simulation and test. Today’s RF semiconductor companies have indeed enough RFIC data points, however the existing data system is fragmented and inconsistent, so impractical for ML training. To overcome this problem, we propose to design an integrated RFIC data system during this phase 1 project. The proposed work sets the foundation for ML algorithm research planned for NIST Phase 2, paving the way for commercializing our novel technology for the modern communications system

* Information listed above is at the time of submission. *

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