Design and Optimization of High-Failure-Current Dual-Direction SCR for Industrial-Level ESD Protection
Bibliographic record
Abstract
In an industrial-grade bus, transient voltage suppressor (TVS) devices that need to withstand inrush currents ensure electrostatic discharge (ESD) reliability of the core chip. This article designs four types of dual-direction silicon-controlled rectifier (DDSCR) device structures based on the 0.5-μm CMOS process. The ESD performance of the TVS device is predicted and verified based on the basic principles of the device, two-dimensional device simulation, and transmission line pulse test results. Four DDSCR structures are embedded with floating N+ to adjust the device's holding voltage window. The results show that the current release capacity of DDSCR_1 is 81.93 mA/μm. The current release capability of DDSCR_2, which has a double-dummy-gate structure, is 82.37 mA/μm. The current release capability of DDSCR_3 of the gate-controlled structure is 86.68 mA/μm. The current release capability of DDSCR_4 of the double-dummy-gate structure and the gate-controlled structure is 86.25 mA/μm. Furthermore, the effect of the size of these devices on the ESD characteristics was studied. The on-resistance of the device structure is calculated by the curve-fitting method. The influence of the dummy gate structure and the gate-controlled structure on the ESD characteristics is analyzed. Finally, the optimal device size to meet the window is found.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".