Real-Time SER Measurements of CMOS Bulk 40- and 65-nm SRAMs Combined With Neutron Spectrometry at the JET Tokamak During Its Final D-T Plasma Operation
Bibliographic record
Abstract
We performed soft error rate (SER) characterization of 40- and 65-nm bulk CMOS static random access memories (SRAMs) combined with neutron spectrometry in the deuterium-tritium (D-T)-fueled Joint European Torus (JET) tokamak during its final D-T plasma operation (September and October 2023) producing a series of several dozens of power pulses. Our experimental results demonstrate the impact of machine operation on the electronics’ reliability, emulating realistic conditions for circuits exposed to the partially radiation-shielded environment of future fusion reactors. Typical bit-flip (BF) rates of 493 h${}^{-1} \cdot $Gbit−1 for 65-nm SRAMs and 2342 h${}^{-1} \cdot $Gbit−1 for 40-nm SRAMs were measured for a residual machine-induced neutron flux of$\sim 3.15\times 10^{5}$cm${}^{-2} \cdot $s−1 below the reinforced concrete slab (thickness of 1045 mm) supporting the tokamak chamber. To complete this characterization work, a general methodology for the SER prediction in such a mixed-field D-T neutron radiation environment composed of both thermal and fast neutrons (FN) (up to 14 MeV) is presented and validated from this ensemble of experimental data for the two SRAM technologies. Finally, the interest in this approach for future tokamaks and high-energy physics accelerators is discussed.
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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 | 0.000 | 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) | 0.001 | 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".