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 A E , the model predicts a spectral density S I n ~ 1/f. For devices with submicron A E , S I n strongly deviates from a 1/f behavior, and several generation-recombination centers dominate the spectrum. However, the average spectrum <S I n >, calculated from several BJTs with identical A E , has a frequency dependence ~ 1/f. The extracted areal trap density within the frequency range 1-10 4 Hz is n T = 3 × 10 9 cm -2 . The simulations show that the condition for observing g-r noise in the spectrum, strongly depends on the number of traps N T , 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 < S I n >, especially for small A E . For A E < 0.1 μ m 2 , the model predicts a relative variation in the noise level [Formula: see text] below <S I n >, and [Formula: see text] above <S I n >. For A E > 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".