Etude de la sensibilité de technologies CMOS/BULK et CMOS/SOI partiellement désertée très largement sub-microniques dans l'environnement radiatif terrestre
Bibliographic record
Abstract
As the dimensions and operating voltages of semiconductor devices are continually reduced to satisfy the increasin demand for higher density and lower, their sensitivity to Soft Error Rates (SER) increases. These non destructive events correspond to a radiation-induced loss of ligical information in a memory cell. In the terrestrial environment, the two major sources of radiations are the atmospheric neutrons and the alpha particles resulting from the natural radioactivty. This study aims at comparing the Soft Error Rates et ground level in Ultra Deep Sub-Micron (UDSM) bulk and commercial Silicon On Insulator (SOI) technologies. Experimental SER tests are perfomed using different irradiation facilities : the Los Alamos neutron source, mono-energic sources of protons and neutrons and radioactive alpha sources, respectively located in the US, Canada and France. Results are given for SRAMs in the CMOS 250, 180, 130 and 90 nm technological nodes. They are used to state on the robustness difference between bulk and SOI up-to-date technologies. The modelling part relies on the rectangular Parallelepiped simulation methodology. Two probalistic Monte-Carlo codes, for the neutrons and alpha particles, were developed and calibrated to determine the key parameters driven the sensitivity of highly integrated technologies. These tools allow to investigate the influence on SER of the critical charge, the sensitive volume, the bipolar effect in SOI and the multiple bit upset rates. Finally, SER predictions are given for future 65 nm technology nodes and a promisingtechnology architecture (Fully Depleted SOI). Hence, the sensitivty of Bulk and SOI technologies is compared down to the 65 nm technological node.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".