Analysis and Measurement of Capacitance Characteristics of a Novel Light-Controlled Dual-Directional Gate Silicon-Controlled Rectifier
Bibliographic record
Abstract
Abstract With the continuous development of optoelectronic devices, parasitic capacitance of electrostatic protection device in the optoelectronic control circuit has become an important factor affecting its response speed. This work designs and manufactures a novel light-controlled dual-directional gate silicon-controlled rectifier (LDGSCR) to study the relationship between light and parasitic capacitance based on 0.18-μm bipolar—complementary metal-semiconductor—double-diffused metal-oxide semiconductor process. The capacitance characteristics of LDGSCR is predicted and verified based on basic principles of the device, 3D device simulation, and capacitance–voltage characteristic C(V) test result. The results show that parasitic capacitance of LDGSCR is affected by both light and bias voltage due to selectivity of J2 junction to light wavelength. The parasitic capacitance of LDGSCR is interestingly divided into three stages of change as voltage increases under light, and the magnitude of capacitance increase varies with the change of light wavelength. In addition, the influence of darkness and light intensity on the parasitic capacitance of LDGSCR is studied. Finally, an optimal adjustment method that balances design windows of novel device and operating frequency of circuit is proposed. This work provides suggestions for the study of capacitance characteristics of light-controlled electrostatic protection devices.
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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".