STATISTICAL SIMULATIONS OF THE LOW-FREQUENCY NOISE IN POLYSILICON EMITTER BIPOLAR TRANSISTORS USING A MODEL BASED ON GENERATION-RECOMBINATION CENTERS
Bibliographic record
Abstract
In this work, a new, physically based model for the low-frequency noise is investigated by statistical simulations. The proposed model is based only on superposition of generation-recombination centers, and can predict the frequency-, current- and area-dependence of the low-frequency noise, as well as the area-dependence of the variation in the noise level. Measurements on Bipolar Junction Transistors (BJTs) are found to be in excellent agreement with the simulated results. For devices with large emitter areas AE, the model predicts a spectral density SIn ~ 1/f. For devices with submicron AE, SIn strongly deviates from a 1/f behavior, and several generation-recombination centers dominate the spectrum. However, the average spectrum In>, calculated from several BJTs with identical AE, has a frequency dependence ~ 1/f. The extracted areal trap density within the frequency range 1-104 Hz is nT = 3 × 109 cm -2. The simulations show that the condition for observing g-r noise in the spectrum, strongly depends on the number of traps NT, as well as the distribution of the corresponding energy level for the traps. The relative noise level is found to vary in a non-symmetrical way around < SIn>, especially for small AE. For AE < 0.1 μ m 2, the model predicts a relative variation in the noise level [Formula: see text] below In>, and [Formula: see text] above In>. For AE > 0.3 μ m 2, the variation is found to be [Formula: see text].
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".