Proton Proxy Risk Tool

Help & User Guide

Preliminary analysis aid. Check the result against your own analysis and, where needed, a test on the intended part. Full disclaimer.

1. What Question Does This Tool Answer?

Many programs test parts or boards with protons only, protons are cheap, penetrating, and don't require delidding. A null proton result is then offered as evidence the hardware is safe against heavy ions. This tool puts a model conditional bound on how much that evidence is worth, and what risk remains, for missions whose heavy-ion exposure matters (anything beyond a benign LEO). It implements the coverage and rate-bounding framework of Ladbury & Lauenstein (IEEE TNS 2016) with the site's CREME96 mission environments.

The core physics: protons cause SEE almost entirely through nuclear-reaction recoils, about 1 recoil per 289,000 protons at 200 MeV. Those recoils are light (Z ≤ 15 in silicon), reach at most ~14–16 MeV·cm²/mg, and have ranges of only a few microns. A proton test is therefore a low-fluence, low-LET, short-range heavy-ion test in disguise. Each of those three deficiencies is modeled here.

2. How It Works

3. Reading the Results

The hero number is the residual worst case: expected SEE over the mission if the part's true behavior is one the proton test could not constrain, capped only by die geometry. The verdict separates what the test demonstrated (the bounded fraction of susceptibility behaviors and their worst-case mission events) from what it did not (the unbounded fraction and its geometric worst case). The coverage chart is useful for identifying gaps in test coverage: wherever the orange mission curve lies above the cyan test-recoil curve, flight will probe LET the test never reached.

OutputMeaning
Recoils on dieTotal recoil ions the test put through the die (Φ/289k at 200 MeV, scaled by energy and area)
Max LET_EQ probedHighest LET_EQ at which the test had statistical power (expected ≥ μUL recoils on the die)
% models unboundedFraction of candidate σ vs. LET behaviors the test cannot constrain, the coverage gap
WC bounded rateWorst mission rate among constrained models at their σ upper limits
WC residual rateWorst mission rate among ALL models, unconstrained ones capped at die area, the residual risk
Area with 10% chance of 0 hitsAreal coverage metric (2016 Sec. II): contiguous die area plausibly never struck by any recoil
Depth sensitivity tableThe same test and mission repeated at assumed depths of 5, 10, 20 and 30 µm: behaviors bounded, the highest LET_EQ probed, and the worst-case events among bounded behaviors and left open. Your own depth is highlighted when it is one of the four

4. Practical Guidance

Choosing the assumed charge-collection depth

The depth d is the input that most changes how much a passed proton test is worth, and it is rarely known for a given part, so the tool labels it an assumption and the SEE mode presets no longer imply that it is known. For a recoil that deposits a given energy, LETEQ = Edep/ρd: doubling d from 10 to 20 µm halves the recoil's equivalent LET, the test probes lower LET, and fewer candidate heavy-ion behaviors are bounded. A depth shallower than the part's generally gives a null test credit it has not earned.

There is a counterpoint in Section 7. Predicting the measured proton cross sections of the paired parts from their heavy-ion curves, the emulator matched best at 5 µm (median predicted/measured ×1.05) and underpredicted at 10 µm (×0.26). That does not establish the true depths of those parts, and for a null result the bounding check (no missed escapes at 2, 5 and 10 µm) is the one that matters; but it means the deeper default is a precaution for judging residual risk, not a claim of better predictive accuracy. The emulator's recoil spectra are digitized at 1, 2, 5, 10 and 30 µm, so 20 µm is interpolated between 10 and 30. Every run shows the depth sensitivity table under the result; where the conclusion generally changes between 10 and 30 µm, the proton test alone cannot support it, and heavy-ion data, process information on the substrate, or an explicit prior are needed.

5. Proton-Informed Monte Carlo (Bayesian Flow)

The worst-case bounds of Sections 2–3 answer "how bad could it be?" The Monte Carlo card answers the complementary question: "given what we historically know about parts like this, how likely is trouble?" Each trial draws a hypothetical part from the same historical prior as the SEL Test-LET tool, technology susceptibility fraction (Extended Historical SEL Priors, EHSP 2026-09, posteriors, selectable by population stratum: modern, all eras, mission-candidate, Space-RHA campaigns), onset-LET CDF, lognormal limiting cross section, CERN Weibull shapes, weights that draw by the Poisson likelihood of your actual proton result, and evaluates its mission rate by CREME96 spectral integration. The reported quantities are the prior (untested) and posterior (post-test) mission SEL probabilities, the posterior probability the part is susceptible at all, and the posterior onset LET, the "rough demonstrated LET threshold": what a passed proton test entitles you to believe about LET₀, given the historical population.

This is the Bayesian supplement the 2016 paper recommends for exactly this situation: explicit priors carrying the assumptions, proton data doing the updating, and the residual quantified as a probability instead of an open question.

6. What the Paired Data Say

The combined SEL test corpus contains 95 parts with both heavy-ion and proton SEL records (92 from published REDW, RADECS and NASA GSFC sources; internal reports enter the counts only). Heavy-ion onset is the uniform LET@10⁻⁸ from the Poisson-Weibull fit where a fit exists, else the author-stated threshold. The tool page shows these numbers live; the case-study page lists every public part.

7. Validation Against Paired Data

The engine was run on the paired parts with a known heavy-ion Weibull (11 parts, 16 proton tests) using the tool's default settings.

Reproducible: the pairing script, adjudications and harness live in the Space-RHA proxy-validation folder (build_proton_hi_pairs.py, build_public_aggregates.py, proxy_validation_harness.js); rerun them as the corpus grows.

8. Accuracy and Limitations

9. References

[1] R. Ladbury and J.-M. Lauenstein, “Evaluating Constraints on Heavy-Ion SEE Susceptibility Imposed by Proton SEE Testing and Other Mixed Environments,” IEEE Trans. Nucl. Sci., vol. 64, no. 1, pp. 301–308, 2016.

[2] R. Ladbury, J.-M. Lauenstein and K. P. Hayes, “Use of Proton SEE Data as a Proxy for Bounding Heavy-Ion SEE Susceptibility,” IEEE Trans. Nucl. Sci., vol. 62, no. 6, pp. 2505–2510, 2015.

[3] D. M. Hiemstra and E. W. Blackmore, “LET Spectra of Proton Energy Levels From 50 to 500 MeV and Their Effectiveness for Single Event Effects Characterization of Microelectronics,” IEEE Trans. Nucl. Sci., vol. 50, no. 6, pp. 2245–2249, 2003.

[4] P. M. O'Neill, G. D. Badhwar and W. X. Culpepper, “Internuclear Cascade-Evaporation Model for LET Spectra of 200 MeV Protons Used for Parts Testing,” IEEE Trans. Nucl. Sci., vol. 45, no. 6, pp. 2467–2474, 1998.

[5] T. L. Turflinger et al. “RHA Implications of Proton on Gold-Plated Package Structures in SEE Evaluations,” IEEE Trans. Nucl. Sci., vol. 62, no. 6, pp. 2468–2475, 2015.

[6] A. H. Johnston, G. M. Swift and L. D. Edmonds, “Latchup in Integrated Circuits from Energetic Protons,” IEEE Trans. Nucl. Sci., vol. 44, no. 6, pp. 2367–2377, 1997.
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