Response of Boost Converters Under Fission-Spectrum Neutron and Gamma Radiation
Bibliographic record
Abstract
Radiation testing of microelectronics remains essential for ensuring reliability in environments such as space and nuclear power systems. One critical component found in many systems is the metal oxide semiconductor field-effect transistor (MOSFET). While work has been conducted on early MOSFET designs, there remains a gap in three key areas: testing modern power MOSFETs, collecting live test data, and evaluating components in combined radiation environments. Using modern components ensures that systems currently in use, both public and private, are better protected against radiation damage. Live monitoring allows observation of single-event effects and transient behavior not detectable through post-irradiation analysis. Additionally, conducting experiments in fission-spectrum neutron and gamma environments better replicates real-world conditions. While the literature addresses each of these topics separately, this work combines them by live-testing a boost converter circuit composed of a MAX1932 gate driver and a BSS119N N-type MOSFET under both neutron and gamma radiation. Testing was performed at the Purdue University Reactor Number One (PUR-1) and the Hopewell Co-60 irradiator, evaluating the circuit’s response to gamma total ionizing dose (TID), thermal neutron-induced transients, and environmental temperature. Live monitoring displayed real-time transients, degradation, and recovery behavior. Results indicate gamma radiation plays a dominant role in circuit degradation. However, when considering the dose dependent degradation, the combined radiation environment of PUR-1 induces failure with 34.8% less dose when compared to the Co-60. Suggesting neutron activation is contributing to secondary gamma dose or 10B (n,α) reaction damage. While Co-60 testing remains critical, thermal neutron facilities may offer a useful, cost-effective screening method for identifying gamma-sensitive components.
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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 | 0.000 | 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 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".