Design Methodology for Multiresonant Gate Drivers: A SPICE-Based Optimization
Bibliographic record
Abstract
This study presents a novel optimization technique based on parameter sweep using a Simulation Program with Integrated Circuit Emphasis (SPICE) for Multi Resonant Gate Drivers (MRGD) that are used in resonant power converters and Switched Mode Power Supply (SMPS) applications. Unlike standard design approaches that use numerical methods as reported in previous studies, the proposed approach streamlines the design process and shortens the product development time by relying on data from simulation models. The proposed optimization is carried out in LTspice, optimizing only the key parameters that affect the frequency response of the multi-resonant filter. The MRGD aims to maximize operational efficiency at high frequencies when used in SMPS and resonant power converters. The proposed concepts are assessed and validated through a hardware prototype. With the help of simulation and hardware verification, the MRGD demonstrated improved efficiency, achieving a 34.8% reduction in gate drive losses compared to an off-the-shelf conventional Voltage Source Gate Driver (VSGD) based on UCC27517.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".