Developing a Universal Mirror–mirror Laser Mapping System for Single Event Effect Research
Bibliographic record
Abstract
Research on single event effects (SEEs) is significant to the design and manufacture of modern electronic devices. By applying two photon absorption (TPA) ultra-fast pulsed lasers, extra electron-hole pairs (EHPs) are generated in a desired location on a chip, simulating the process that could occur in the circuit by energetic particles. In this study, a SEE sensitivity mapping system is described which uses this method to generate real-time sensitivity maps for various electronic devices. The system hardware includes an attenuator to control the energy, a Pockels cell as a fast-optical switcher and a mirror–mirror module to project the laser beam into a certain location. The system software developed for this application controls the laser system, automatically generates sensitivity maps, communicates with the testing devices and logs the SEE results. The two main features of this laser mapping system are: high scanning velocity for large area scanning (about 1 × 1 mm) and high spatial resolution for small area scanning (about 1 × 1 μm). To verify this mapping system, sensitivity maps were generated for static random access memory (SRAM) built with 65 nm technology and for commercial operational amplifiers (op-amps). The achieved sensitivity maps were compared with circuitry analysis and laser testing results, confirming this mapping system to be effective.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".