Electronics From the Bottom Up: Strategies for Teaching Nanoelectronics at the Undergraduate Level
Bibliographic record
Abstract
Nanoelectronics is an emerging area of electrical and computer engineering that deals with the current-voltage behavior of atomic-scale electronic devices. As the trend toward ever smaller devices continues, there is a need to update traditional undergraduate curricula to introduce electrical engineers to the fundamentals of the field. These fundamentals encompass topics from quantum mechanics and condensed-matter physics, and they pose new teaching challenges in electronics education; specifically, unconventional ideas must be presented in a rapid and yet complete way so that engineering undergraduates can quickly yet satisfyingly absorb the key concepts, and then apply these concepts to emerging devices. This paper describes the strategies employed by the author in teaching the subject to large undergraduate classes at his institution. These strategies include the use of computer visualization, a careful introduction of quantum mechanics, and a constant demonstration of the relevance of theory by practical examples and calculations. The effectiveness of the approach is illustrated through survey results of the Universal Student Ratings of Instruction at the author's institution and by way of typical assignment and exam questions that demonstrate the level of sophistication that students can attain in what might otherwise be viewed as a purely mathematical and esoteric subject.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".