Bibliographic record
Abstract
The intent of this paper is to give an overview of the place of reverse engineering (RE) in the semiconductor industry, and the techniques used to obtain information from semiconductor products. The continuous drive of Moore's law to increase the integration level of silicon chips has presented major challenges to the reverse engineer, obsolescing simple teardowns and demanding the adopted of new and more sophisticated technology to analyse chips. This trend is continuing; the 2006 update of the International Technology Roadmap for Semiconductors is predicting the shrinkage of transistor gates from the current 65-nm generation to 16 nm at the turn of the decade, and the usage of over 1.5 billion transistors in high-volume microprocessor chips. The paper covers product teardowns, and discusses the techniques used for system-level analysis, both hardware and software; circuit extraction, taking the chip down to the transistor level and working back up through the interconnects to create schematics; and process analysis, looking at how a chip is made, and what it is made of. Examples are also given of each type of RE.
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 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 | 0.001 | 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) | 0.001 | 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 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".