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Revolutionary RF Circuit Simulator for New Electronic Design and Analysis Capabilities

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-22-C-0033
Agency Tracking Number: A2-9105
Amount: $1,149,875.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: A20B-T002
Solicitation Number: 20.B
Timeline
Solicitation Year: 2020
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-06-01
Award End Date (Contract End Date): 2024-05-31
Small Business Information
601 Hutton St STE 109
Raleigh, NC 27606-6322
United States
DUNS: 148551653
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Nikhil Kriplani
 (919) 341-8241
 nikhil.kriplani@vaduminc.com
Business Contact
 Aaron Walker
Phone: (919) 341-8241
Email: aaron.walker@vaduminc.com
Research Institution
 North Carolina State University
 Rhett Davis
 
2701 Sullivan Dr., Suite 240, Campus Box 7514
Raleigh, NC 27695-7514
United States

 (919) 515-5857
 Nonprofit College or University
Abstract

Vadum and North Carolina State University (NCSU) will develop Simulation of Communications Circuits in the Time Domain using Reinforcement Learning (SCOUTER) – a novel machine-learning-enhanced RF circuit simulator that rapidly and accurately analyzes transient circuit behavior using complex time-frequency communications waveforms. SCOUTER will have the capability to simulate modern RF transceivers in the time domain with extremely high-dynamic range (> 160 dB), while capturing full RF device nonlinearity and multi-physics effects. The use of macro-models of nonlinear RF device components significantly shortens simulation execution time with minimal loss in fidelity. A novel neural-network-based multi-scale transient simulation enables high dynamic range for analysis of nonlinear effects in the presence of complex waveforms. The core simulation component will be augmented with automated reinforcement learning to discover novel RF phenomena in representative RF circuits of interest. The learning approach searches the multi-dimensional space of input waveform parameters, resulting in a capability that rigorously characterizes modern, complex RF circuits more rapidly and accurately than existing state-of-the-art techniques.

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

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