An Improved Double Pulse Circuit Model for Predicting Accurate Dynamic Behaviour and Switching Losses Of Mosfets

Accurate PowerMOS SPICE models and reliable circuits for device prototyping are crucial to provide meaningful feedback to platform developers. System simulation is key in speeding up the delivery of new technologies to a fast-changing market. Double pulse is a widely used system for dynamic performance investigation, but a simplified schematic of the measurement circuit has insufficient accuracy. In this paper, an improved double pulse schematic is proposed which includes an appropriate gate driver and parasitic elements from both the Printed Circuit Board (PCB) and discrete devices. A new method to extract these parasitic effects is demonstrated. Our study reveals that with this approach the gap between simulation and experimental data is reduced from 25% to 8% [1].

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Published
2024-11-18
DOI
https://doi.org/10.1109/dmc62632.2024.10812132
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

An Improved Double Pulse Circuit Model for Predicting Accurate Dynamic Behaviour and Switching Losses Of Mosfets

Buket Turan Azizoğlu, Neha Sharan, Christian Radici, Samir Chowdhury et al.
3 links
Advanced Memory and Neural Computing
article

An Improved Double Pulse Circuit Model for Predicting Accurate Dynamic Behaviour and Switching Losses Of Mosfets

Buket Turan Azizoğlu, Neha Sharan, Christian Radici, Samir Chowdhury, Ian Stubbs, John Neeko Garlitos
article en

Abstract

Accurate PowerMOS SPICE models and reliable circuits for device prototyping are crucial to provide meaningful feedback to platform developers. System simulation is key in speeding up the delivery of new technologies to a fast-changing market. Double pulse is a widely used system for dynamic performance investigation, but a simplified schematic of the measurement circuit has insufficient accuracy. In this paper, an improved double pulse schematic is proposed which includes an appropriate gate driver and parasitic elements from both the Printed Circuit Board (PCB) and discrete devices. A new method to extract these parasitic effects is demonstrated. Our study reveals that with this approach the gap between simulation and experimental data is reduced from 25% to 8% [1].

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Advanced Memory and Neural Computing
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