Behavioral modeling for stability in multi-chip power modules

Mazzola, Michael S.; Rahmani, Maryam; Gafford, James; Lemmon, Andrew N.; Graves, Ryan

doi:10.1109/iwipp.2015.7295985

Cited by 12 publications

(3 citation statements)

References 4 publications

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“…LF simulation tools such as Q3D Extractor have been frequently used for extracting HF effects contained in fast switching transients. However, the needs to resort to radio frequency modeling techniques for predicting rather fast switching transients of power devices have been recognized [7], [8] and HF EM tools such as e.g. ANSYS HFSS attract more and more attention.…”

Section: Electromagnetic Modeling Toolsmentioning

confidence: 99%

Tools for Broadband Electromagnetic Modeling of Power Semiconductor Packages and External Circuit Layouts

Kovacevic-Badstuebner

Großner

Popescu

2020

2020 32nd International Symposium on Power Semiconductor Devices and ICs (ISPSD)

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Advanced power electronics applications using fast switching power semiconductor devices imply the needs for electromagnetic (EM) tools for extracting the parasitics of twoand three-dimensional geometries (i.e. PCB layouts and packages) in an accurate and computationally efficient way. This paper presents a comprehensive analysis of commercially available EM tools (ANSYS Q3D and HFSS) for broadband modeling of circuit and package layout parasitics. The coupling of layout EM models and device models are performed in a circuit simulation environment. It is demonstrated that an accurate modeling of layout parasitics in the range above 100 MHz is required in order to predict the fast switching performance of power semiconductor devices more precisely and this cannot be achieved by the lowfrequency EM tools.

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Section: Electromagnetic Modeling Toolsmentioning

confidence: 99%

Tools for Broadband Electromagnetic Modeling of Power Semiconductor Packages and External Circuit Layouts

Kovacevic-Badstuebner

Großner

Popescu

2020

2020 32nd International Symposium on Power Semiconductor Devices and ICs (ISPSD)

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“…Using the prescribed techniques, it was possible to curve-fit each terminal pair in the module to a series RLC equivalent model. As discussed in the previous sections, the effective capacitance is contributed by the paralleled HEMT and anti-parallel diode intrinsic capacitance, and the effective inductance is the series and parallel combination of the wire bonds and interconnects structures within the module [35,36].…”

Section: Dut Device Under Testmentioning

confidence: 99%

An RF Approach to Modelling Gallium Nitride Power Devices Using Parasitic Extraction

et al. 2020

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This paper begins with a comprehensive review into the existing GaN device models. Secondly, it identifies the need for a more accurate GaN switching model. A simple practical process based on radio frequency techniques using Vector Network Analyser is introduced in this paper as an original contribution. It was applied to extract the impedances of the GaN device to develop an efficient behavioural model. The switching behaviour of the model was validated using both simulation and real time double pulse test experiments at 500 V, 15 A conditions. The proposed model is much easier for power designers to handle, without the need for knowledge about the physics or geometry of the device. The proposed model for Transphorm GaN HEMT was found to be 95.2% more accurate when compared to the existing LT-Spice manufacturer model. This work additionally highlights the need to adopt established RF techniques into power electronics to reduce the learning curve while dealing with these novel high-speed switching devices.

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“…Additionally, the RF model is developed, neglecting the effect of certain parasitic elements [5], [6]. Due to the complexity of the device structure, time involved in parasitic extraction and the analytical procedures involved, this model is not suitable for validating all applications [7], [8]. Thus, it cannot serve as a universal model for GaN.…”

Section: Introductionmentioning

confidence: 99%

Gallium Nitride Power Electronic Devices Modeling Using Machine Learning

Hari

Ahsan

Sridhar³

et al. 2020

IEEE Access

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A state-of-the-art Machine Learning (ML) based approach, by modeling the behavior of Gallium Nitride (GaN) power electronic devices, is presented in this paper. Switching voltage and current waveforms of these novel devices are accurately predicted using the developed supervised ML algorithm. This was utilised to build a more generic black-box model for these devices. Moreover, long short-term memory unit (LSTM) and gated recurrent unit (GRU) device models have been proposed to make the approach more user friendly. The performance of the developed approach is verified using a set of simulations and experimental tests under 450 V, 10 A test conditions. Model results demonstrate an error rate of 0.03 and convergence speed of 3s with excellent stability. Compared to the existing models, the developed ML-based model produces more accurate results, converges faster and has a better stability. Additionally, the developed ML-based GaN model offers the ability to select the best fit available GaN model (Panasonic, GaN Systems, Transphorm etc.). It automatically configures them into a system that would optimally yield the desired power conversion. This enables a shorter learning curve for the power electronics community, which would lead to acceptance and faster adoption of these devices by the power electronics industry.INDEX TERMS Gallium Nitride, power electronics, modelling, machine learning, neural networks.

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Behavioral modeling for stability in multi-chip power modules

Cited by 12 publications

References 4 publications

Tools for Broadband Electromagnetic Modeling of Power Semiconductor Packages and External Circuit Layouts

Tools for Broadband Electromagnetic Modeling of Power Semiconductor Packages and External Circuit Layouts

An RF Approach to Modelling Gallium Nitride Power Devices Using Parasitic Extraction

Gallium Nitride Power Electronic Devices Modeling Using Machine Learning

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