This paper motivates the development of sophisticated data–driven models for power magnetic material characteristics.Core losses and hysteresis loops are critical information in the design process of power magnetics, yet the physics behind them is not fully understood or directly applicable. Both losses and hysteresis loops change for each magnetic material and depend heavily on electrical operating conditions (e.g., waveform, frequency, amplitude, dc bias), mechanical properties (e.g., pressure,vibration), temperature, and geometry of the magnetic components,and in a nonlinear and coupled fashion. Understanding the complex and intertwined relationship these factors have on core loss is important for the development of accurate models and their applicability and limitations. Existing studies on power magnetics are usually developed based on a small amount of data and do not reveal the full magnetic behavior across a wide range of operating conditions. In this paper, based on a recently dev loped large-scale open-source database– <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagNet –the core losses and hysteresis loops of Mn–Zn ferrites are analyzed over a wide range of amplitudes, frequencies, waveform shapes, dc bias levels, and temperatures, to quantify the complexity of modeling magnetic core losses, amplitude permeability, and hysteresis loops and provide guidelines for modeling power magnetics with datadriven methods.

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Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics

This article applies machine learning to power magnetics modeling. We first introduce an open-source database—MagNet—which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions, consisting of more than 500 000 data points in its current state. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling the core losses and <inline-formula><tex-math notation="LaTeX">$B$</tex-math></inline-formula>–<inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula> loops. The neural network allows multiple factors that may influence the magnetic characteristics to be modeled in a unified framework, where the nonlinear behaviors are captured with high accuracy and high generality. Neural network models are found to be effective in compressing the measurement data and predicting the material characteristics, paving the way for “neural networks as datasheets” to assist power magnetics design. Transfer learning is applied to the training of neural network models to further reduce the data size requirement while maintaining sufficient model accuracy.

How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics

This article presents the MagNet-AI platform as an online platform to demonstrate the “neural network as datasheet” concept for <inline-formula><tex-math notation="LaTeX">$B$</tex-math></inline-formula>–<inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula> loop modeling and material recommendation of power magnetics across wide operation range. Instead of directly presenting the measured characteristics of magnetic core materials as time sequences, we employ a neural network to capture the <inline-formula><tex-math notation="LaTeX">$B$</tex-math></inline-formula>–<inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula> loop mapping relationships of magnetic materials under different excitation waveforms at different temperatures and dc bias. Long short-term memory and transformer-based neural network models are developed, verified, and compared. The neural network can be used to rapidly predict hysteresis loops and core losses under different operating conditions, compare materials, and recommend materials for design. The neural network model is also proved effective in reconstructing the raw measurement while accurately maintaining the magnetic characteristics, enabling rapid material evaluation and comparison.

MagNet-AI: Neural Network as Datasheet for Magnetics Modeling and Material Recommendation

This paper presents a multistack switched-capacitor point-of-load (MSC-PoL) voltage regulation module (VRM) with coupled magnetics for ultrahigh-current chiplet systems. In the MSC-PoL architecture, the stacked switched-capacitor cells split the high input voltage into several intermediate voltage rails, which are loaded with the switched-inductor cells to achieve soft charging and voltage regulation. Automatic capacitor voltage balancing and inductor current sharing are realized during the soft charging process. Many inductors of the switched-inductor cells are coupled into one and operated in interleaving to reduce the inductor current ripple and boost the transient speed. A 48-to-1-V/450-A VRM containing two MSC-PoL modules is built and tested, leveraging high voltage GaN devices for the front-end and high current Silicon devices for the back-end. Two ladder-structured coupled inductor designs are developed and compared, one of which installs a leakage magnetic plate to adjust the leakage inductance for lower current ripple. Featuring 3D stacked packaging, the entire power stage, gate drivers, and bootstrap circuits of one MSC-PoL module are enclosed into a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{1}{16}$</tex-math></inline-formula> -brick/0.31-in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> /6-mm-thick package. The peak and the full-load efficiencies as well as the full-load power density (including both gate loss and size) of the MSC-PoL prototype with and without using the leakage plate are 91.7% and 89.5%, 85.8% and 85.6%, and 621 W/in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> and 724 W/in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> , respectively. The 6-mm-thick MSC-PoL converter can be embedded into the chiplet or CPU socket, enabling power-supply-in-package (PwrSiP) for extreme efficiency, density, and control bandwidth.

MSC-PoL: Hybrid GaN-Si Multistacked Switched Capacitor 48V PwrSiP VRM for Chiplets

The non-linear property of magnetics poses challenges for their loss modelling in power electronics due to lacking full physical models. As a practical approach for their loss estimation, the manufacturers can pre-measure the losses in standalone rigs and distribute the "loss maps" as interpolated look-up tables/curves for the end users. However, with more factors discovered that impact the losses, e.g., DC bias and load conditions, the dimensions of the loss maps cannot be solved by conventional surface/curve fitting methods. This paper addresses this problem by applying the Artificial Neural Network (ANN). For both inductors and transformers, Neural Network-aided loss maps (NNALMs) are designed and evaluated with comparisons against conventional loss maps to reveal the limitations of the latter caused by physically intercoupled input variables. The NNALMs not only show superior accuracy throughout the whole datasets, but also enable the loss maps to expand the dimensions to account for more factors (e.g., load conditions in transformers) and generate multiple outputs (e.g., both the winding loss and core loss). The ANN-aided loss maps can be distributed as digitized datasheets of standardized magnetics, enabling rapid, accurate and user-friendly loss estimations for power electronics engineers. 

Artificial Neural Network Aided Loss Maps for Inductors and Transformers

This paper presents the concept of “Neural Network as Datasheet” for B- H loop modeling of power magnetics with sequence-to-sequence machine learning. Instead of directly pre-senting the measured characteristics of magnetic core materials, we employ a neural network to capture the B-H loop mapping relationships of magnetic materials under different excitation waveforms at different temperatures. The training and inference process of the neural network are fully automated to minimize the impact of human error. Neural networks are also effective in compressing the information contained in the raw database to avoid data search or interpolation. The neural network can be used to rapidly predict B- H loops under different operating conditions and support circuit simulations. Based on a recently developed large-scale magnetic core loss database - MagNet - we demonstrate that a neural network datasheet can effectively compress and release information about power magnetics and can play important roles in power electronics converter design.

Neural Network as Datasheet: Modeling B-H Loops of Power Magnetics with Sequence-to-Sequence LSTM Encoder-Decoder Architecture

This paper introduces an open-source database - MagNet - for data-driven magnetic core loss modeling. MagNet aims to support data-driven magnetics research by hosting a large amount of experimentally measured excitation waveform data for many materials across a variety of operating conditions. This database in its current state contains over 150,000 excitation waveforms for six ferrite materials - TDK{N27, N49, N87}, Ferroxcube{3C90, 3C94}, Fair-Rite{78} - in the 50 kHz to 500 kHz, 10 mT to 300 mT range for sinusoidal, triangle, and trapezoidal waveforms. This paper presents the purposes of building MagNet, introduces the data acquisition system and data format, discusses the data quality, and presents a few examples of using this database with data driven methods.

MagNet: An Open-Source Database for Data-Driven Magnetic Core Loss Modeling

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