SBIR-STTR Award

Revolutionary RF Circuit Simulator for New Electronic Design and Analysis Capabilities
Award last edited on: 9/9/2023

Sponsored Program
STTR
Awarding Agency
DOD : Army
Total Award Amount
$1,316,343
Award Phase
2
Solicitation Topic Code
A20B-T002
Principal Investigator
Nikhil Kriplani

Company Information

Vadum Inc

601 Hutton Street Suite 109
Raleigh, NC 27606
   (919) 341-8241
   info@vaduminc.com
   www.vaduminc.com

Research Institution

North Carolina State University

Phase I

Contract Number: W911NF-21-P-0024
Start Date: 12/1/2020    Completed: 5/31/2021
Phase I year
2021
Phase I Amount
$166,468
Vadum and North Carolina State University (NCSU) will develop a novel machine-learning enhanced nonlinear RF circuit simulation capability to comprehensively analyze circuit behavior on generic time-frequency communications waveforms. The simulation will cover high-dynamic range, full RF device nonlinearity, multi-physics effects and time delay effects. Macro-models will significantly shorten simulation execution time with minimal loss in fidelity. A machine-learning encapsulation will aid in discovering novel RF phenomena.

Phase II

Contract Number: W911NF-22-C-0033
Start Date: 6/1/2022    Completed: 5/31/2024
Phase II year
2022
Phase II Amount
$1,149,875
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.