The temperature dependence of the dc characteristics of silicon germanium bipolar transistors
Bibliographic record
Abstract
Silicon germanium bipolar transistors provide excellent high frequency performance within the context of silicon, encroaching dramatically on III–V semiconductor’s territory. Applications of the technology, however, will require the performance specifications with regard to the temperature range of operation. Using devices accessible from a BiCMOS technology, adapted to incorporate SiGe, we have measured the temperature dependence of properties such as the dc gain, ideality factors, and VBE at various base current levels, from 100 to 450 K. The ideality factors are close to unity above 250 K but greater than 2.5 at 100 K. We suggest that tunneling at the emitter perimeter can account for the high base ideality factors at low current and low temperature. Our data show a decrease in the gain with increasing current and temperature in normal operating ranges—devices with “box” Ge profiles in the base layer do not have this feature. This is important in preventing thermal runaway in power transistors, and may remove the necessity of emitter ballast resistors and provide higher power-added-efficiency in an amplifier block. We also show that the gain-Early voltage product is roughly linear with 1/T above ambient temperature.
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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) | 0.000 | 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) | 0.002 | 0.001 |
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".