Bibliographic record
Abstract
The capacitance of a transistor is a crucial consideration when designing devices for applications in the commercially and societally important areas of digital logic, high-frequency signal processing, and memory. Accordingly, as a pre-cursor to the subsequent chapters on transistors suited to these applications, transistor capacitance is given a thorough treatment in this chapter of its own. The approach taken presents capacitance in a general way that can be applied to all transistors. The usual practice is to treat capacitance in an ad hoc manner, sometimes involving charges of opposite polarity, as in junction capacitance, and sometimes considering just one polarity of charge, as in storage capacitance, for example. In fact, the origin of these two capacitances is the same: in the case of the emitter/base capacitance, for example, it is the change in charge within the device due to electrons that have entered from the emitter to set-up a new steady-state charge profile in the transistor in response to a change in base potential. This fact is recognized here, and provides a view of capacitance that is both physically based and intuitively appealing. Our approach is based on that of Tsividis for MOSFETs, and leads naturally to a double-subscripted specification of capacitance in all types of transistor, e.g., C EB or C SG . Thus, a 3×3 capacitance matrix captures all the capacitive elements of a three-terminal device, and allows for non-reciprocity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.027 |
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".