Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design

Guillod, Thomas; Papamanolis, Panteleimon; Kolar, Johann W.

doi:10.1109/ojpel.2020.3012777

Cited by 148 publications

(61 citation statements)

References 34 publications

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“…Artificial Neural Networks (ANNs) is a common machine learning technique and has a history of serving as compact models for semiconductor devices [36][37][38]. As shown in Fig.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…As shown in Fig. 3(a), an artificial neuron propagates a biased weighted sum of inputs to activation function and generates an output, where the and are the weights and biases; the activation function can be Rectified Linear Unit (ReLU) or sigmoid functions [36][37][38]. Multi-layer Perceptron (MLP) is a typical example of a feedforward artificial neural network.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Magnetic Core TSV-Inductor Design and Optimization for On-chip DC-DC Converter

Wen

Xiao

Chen

et al. 2022

ACM Trans. Des. Autom. Electron. Syst.

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The conventional on-chip spiral inductor consumes a significant top-metal routing area, thereby preventing its popularity in many on-chip applications. Recently through-silicon-via (TSV) based inductor (also known as TSV-inductor) with a magnetic core has been proved to be a viable option for the on-chip DC-DC converter. The operating conditions of these inductors play a major role in maximizing the performance and efficiency of the DC-DC converter. However, there is a critical need to study the design and optimization details of magnetic core TSV-inductors with the unique 3D structure embedding magnetic core. This paper aims to provide a clear understanding of the modeling details of a magnetic core TSV-inductor and a design and optimization methodology to assist efficient inductor design. Moreover, a machine learning assisted model combining physical details and artificial neural network (ANN) is also proposed to extract the equivalent circuit to further facilitate DC-DC converter design. Experimental results show that the optimized TSV-inductor with the magnetic core and air-gap can achieve inductance density improvement of up to 7.7 × and quality factor improvements of up to 1.6 × for the same footprint compared with the TSV-inductor without a magnetic core. For on-chip DC-DC converter applications, the converter efficiency can be improved by up to 15.9% and 6.8% compared with the conventional spiral and TSV-inductor without magnetic core, respectively.

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“…Artificial Neural Networks (ANNs) is a common machine learning technique and has a history of serving as compact models for semiconductor devices [36][37][38]. As shown in Fig.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Magnetic Core TSV-Inductor Design and Optimization for On-chip DC-DC Converter

Wen

Xiao

Chen

et al. 2022

ACM Trans. Des. Autom. Electron. Syst.

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show abstract

“…For the former, an emerging equivalent prediction fitting approach based on machine learning (ML) and neural networks (NNs) represents a novel way of circuit transient solution, which has been applied in power electronic applications [20], [21]. After learning from given datasets, the NNs produce the Pareto fronts and select the optimal designs, which means the explicit physical significance is only contained inside the input and output variables but not NN itself [22]. In the latter case, as opposed to CPUs that adopt fixed computing architecture, field-programmable gate arrays (FPGAs) provide an intrinsic parallelism without predefined hardware architecture causing them to be the ideal real-time emulation hardware acceleration platform for the parallelization of NNs [20].…”

Section: Introductionmentioning

confidence: 99%

Real-Time ML-Assisted Hardware-in-the-Loop Electro-Thermal Emulation of LVDC Microgrid on the International Space Station

Chen

Zhang

Dinavahi

2022

IEEE Open J. Power Electron.

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For being the world's largest low voltage direct current (LVDC) microgrid (MG) in space, the power generation and distribution systems aboard the International Space Station (ISS) employ a hierarchical assortment of electric power sources, energy storage, control devices, power electronics, and loads operating cooperatively at multifarious system dispositions and multi-stage configurations. At the early phase of design, for such time-critical systems, the trade-off between reliability and convergence rate of device modeling, varying accuracy requirements of control flows, and especially the implementation for real-time performance have brought new challenges and problems for testing and validation of the MG. One of the solutions presented by this paper is to use the hardware-in-the-loop (HIL) emulation, where the MG is emulated using the field-programmable gate array (FPGA) hardware platform. In parallel with the emulation effort, comprehensive modeling solutions for both large-scale photovoltaic (PV) solar array wings (SAWs) and nonlinear behavior model (NBM) of insulated-gate bipolar transistors (IGBTs) have been utilized based on machine learning (ML) concepts of artificial neural network (ANN) and recurrent neural network (RNN). Both system-level (validated by Matlab/Simulink) and device-level (validated by SaberRD) transient simulations are carried out, and the results exhibit high accuracy and fidelity of the models and significant improvements in execution speed and hardware resource consumption.INDEX TERMS Artificial neural network (ANN), electromagnetic transients, field-programmable gate arrays (FPGAs), hardware-in-the-loop (HIL), insulated-gate bipolar transistors (IGBTs), International Space Station (ISS), low voltage direct current (LVDC), machine learning (ML), microgrid (MG), photovoltaic, recurrent neural network (RNN), renewable energy, real-time systems, solar array wings (SAWs).

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“…An optimal sizing can be achieved through calculation tools able to consider nonlinearity, magnetic hysteresis, and the real non-uniform distribution of the magnetic induction in the component core, with acceptable accuracy [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Influence of Non-Linearity in Losses Estimation of Magnetic Components for DC-DC Converters

et al. 2021

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In this paper, the problem of estimating the core losses for inductive components is addressed. A novel methodology is applied to estimate the core losses of an inductor in a DC-DC converter in the time-domain. The methodology addresses both the non-linearity and dynamic behavior of the core magnetic material and the non-uniformity of the field distribution for the device geometry. The methodology is natively implemented using the LTSpice simulation environment and can be used to include an accurate behavioral model of the magnetic devices in a more complex lumped circuit. The methodology is compared against classic estimation techniques such as Steinmetz Equation and the improved Generalized Steinmetz Equation. The validation is performed on a practical DC-DC Buck converter, which was utilized to experimentally verify the results derived by a model suitable to estimate the inductor losses. Both simulation and experimental test confirm the accuracy of the proposed methodology. Thus, the proposed technique can be flexibly used both for direct core loss estimation and the realization of a subsystem able to simulate the realistic behavior of an inductor within a more complex lumped circuit.

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Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design

Cited by 148 publications

References 34 publications

Magnetic Core TSV-Inductor Design and Optimization for On-chip DC-DC Converter

Magnetic Core TSV-Inductor Design and Optimization for On-chip DC-DC Converter

Real-Time ML-Assisted Hardware-in-the-Loop Electro-Thermal Emulation of LVDC Microgrid on the International Space Station

Influence of Non-Linearity in Losses Estimation of Magnetic Components for DC-DC Converters

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