Machine Learning-Enabled Fast Prediction of GGNMOS Performance and Inverse Design for Electrostatic Discharge Applications
Bibliographic record
Abstract
Electrostatic discharge (ESD) protection is generally required in integrated circuit (IC) chips. The grounded-gate n-channel metal-oxide-semiconductor (GGNMOS) is a popular device for ESD protection in circuit design. However, the design optimization of GGNMOS for every circuit is carried out using the trail-and-error method and requiring iterative device simulations. The device simulation is usually performed using technology computer-aided design (TCAD) software and is time-consuming. To address this issue, we developed machine learning models for fast prediction of GGNMOS performance and inverse design of its structure according to performance metrics. Here, we generated data for machine learning using Sentaurus TCAD. We applied AutoML and weight-sharing deep neural networks to predict current-voltage (I-V) characteristics of GGNMOS and extract performance metrics: triggering point (It1,Vt1), holding point (Ih,Vh), thermal breakdown point (It2,Vt2), and discharge rsistanceRon. Additionally, Bayesian optimization was employed for inverse design, allowing rapid identification of optimal structural parameters for desired performance metrics. This approach significantly accelerates the ESD design process, minimizing dependency on costly and time-consuming TCAD simulations. Our work represents an advancement in design of electronic devices and circuits using machine learning.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".