Design and testing of a pulsed laser system for single event effects analysis on semiconductor devices
Why this work is in the frame
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Bibliographic record
Abstract
Single event upsets (SEUs) are a critical concern for reliability in semiconductor devices, particularly as technology nodes shrink and devices become more susceptible to cosmic rays and other radiation sources. Understanding and mitigating these effects are crucial for ensuring the reliability of electronic systems. By leveraging pulsed laser charge injection, researchers can achieve results comparable to traditional methods like heavy ion testing but at a lower cost and with better control over spatial and temporal parameters. The photogeneration effect in semiconductor devices due to single photon absorption and two-photon absorption is introduced and the test system designed to make use of both type of charge photogeneration is implemented. The design and development of the pulsed laser system at the Saskatchewan Structural Sciences Centre (SSSC) is described. The system's capabilities, such as precise control over pulse parameters and spatial targeting, make it a valuable tool for single event effect (SEE) analysis. The testing of a static random access memory (SRAM) using this newly developed system provides valuable insights into the device's behavior under radiation-induced faults. By quantifying overall error rates, including multiple-bit errors and faults in memory and control circuitry, researchers can assess the device's susceptibility to SEUs. Comparing the pulsed laser results of SRAM testing with those obtained from heavy ion testing demonstrates the effectiveness of the pulsed laser system. Overall, the new and unique Pulsed Laser System at the SSSC represents a significant advancement in SEE testing capability. Its ability to provide results comparable to traditional methods while offering greater accessibility and control has the potential to drive further innovation in the field of radiation effects analysis in semiconductor devices.
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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 | 0.000 | 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) | 0.000 | 0.000 |
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 it