MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics

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Princeton, New Jersey
Project Term:
05/01/2020 - 11/30/2022

Technology Description:

The Princeton University team will use machine learning-enabled methods to transform the modeling and design methods of power magnetics and catalyze disruptive improvements to power electronics design tools. They will develop a highly automated, open-source, machine learning-based magnetics design platform to greatly accelerate the design process, cut the error rate in half, and provide new insights to magnetic material and geometry design. Princeton’s Simulation Program with its Integrated Circuit Emphasis-based, or SPICE-based modeling platform, will utilize a highly automated data acquisition testbed capable of measuring a large number of magnetic cores with a wide range of electrical circuit excitations, a machine-learning trained modeling method for modeling the core loss and saturation effects of magnetic materials, and a computer-aided-design tool which can synthesize the SPICE netlist for planar magnetics.

Potential Impact:

DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. If successful, DIFFERENTIATE will yield the following benefits in ARPA-E mission areas:


Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.


Use these tools to solve our most challenging energy and environmental problems by facilitating an economically-attractive transition to lower carbon-footprint energy sources and systems.


Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.


ARPA-E Program Director:
Dr. David Tew
Project Contact:
Prof. Minjie Chen
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Project Contact Email:

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