Power-Diode Extreme-Regime I–V Extrapolation

You are an AI agent assisting the device engineer who must qualify a new power-diode lot at a semiconductor supplier. The probe station covers only a safe bias/temperature window — 290–325 K, reverse bias to −3.6 V — but the qualification sign-off due this quarter must commit leakage and conduction numbers at mission-profile extremes: 385–400 K and reverse bias to −26 V, where measurement is destructive and the burn-in rig is booked for months. Overstate the lot's capability and an automotive customer eats field returns; understate it and the socket goes to a competitor. You inherit a starting prediction method that extrapolates a fitted window trend; your job is to build the method the lab will run on every future lot — it is re-run, unchanged, on qualification devices whose extreme-regime behavior you never see.

Hard Constraints

• Submit a method: methods/main/solver.py defining exactly predict(record) -> list of floats — the predicted log10 of the absolute terminal current in amperes at each point of record["extreme"], in order; a pure function of one device record.
• record is a dict with the device's safe-window measurements (window: 56 {T, V, I} points) and the target grid (extreme: {T, V} points) — the same schema as the practice records, minus the truth field.
• Each call runs in a fresh process under a 180-second wall-clock budget (measured outside your process). Anything that takes longer than 195 s (budget + 15 s grace) is scored as the worst case for that device, and the process is hard-killed at 210 s regardless; crashed or wrongly-shaped outputs are scored the same worst case. The grader records a timeout and the child's exit status separately from "produced no output", so both show up in the run log.
• The whole grading stage is capped at 7200 s wall-clock, inside a container declared at 4 CPUs / 1024 MiB — the same shape as your own container, so a method that fits here fits there. Devices are graded serially, one subprocess at a time: with 16 qualification devices the stage budget is the binding constraint only if your average device takes more than ~7.5 minutes, which the 180 s per-device budget already forbids.
• A note on shared memory: this container's /dev/shm is 64 MiB and cannot be enlarged. A multiprocessing.shared_memory / joblib memmap segment larger than that is created successfully and then faults (SIGBUS) on first write, killing your process with no output. Pass large arrays to workers by fork inheritance or through ordinary files in TMPDIR instead. The grader reports a SIGBUS kill as its own failure reason, distinct from "produced no output".
• The graded lot is 16 devices against your 24 practice devices — about 0.67x the visible count, on the identical 12-point extreme grid. Grading is therefore cheaper than one full selfcheck.py pass; sizing your method against your own self-check wall-clock is safe here.
• CPU only, no network. Runtime: Python 3 with numpy and scipy, plus the DEVSIM TCAD device simulator (import devsim) — build physics models of the lot if you choose to. BLAS/OpenMP threading is pinned to 4 in both this image and the grader, so your local timings match the graded ones.
• Only files under methods/ are collected and re-run: keep everything predict() imports inside methods/main/. Do not modify data/, examples/, selfcheck.py or run_solver.py.

What You Have

• data/practice/instances.json — 24 practice devices with complete extreme-regime truth (noise-free log10|I| on the full 12-point extreme grid), documented field by field in data/practice/DATA_CARD.md. This is your only labeled data; study it in full.
• data/qualification/records.json — the 16 qualification devices your method will actually be judged on: same window protocol, same extreme grid, no truth. They are a fresh draw from the same production population as the practice fleet, with part of the lot weighted toward the harder corner of that same population.
• examples/devsim_diode_demo.py — a working DEVSIM drift-diffusion model of this device architecture (mesh, doping, equation assembly, bias ramping with convergence back-off, temperature stepping, terminal current readout). A starting point, not a prescription.
• methods/main/solver.py — the inherited starting method: a log-space window-trend fit extrapolated in temperature with the voltage law clamped at the window edge. It carries a real trend signal but is far from what the records support; its level is also the floor you must clearly beat before the evaluation awards any credit.
• python3 selfcheck.py — free and unlimited: scores your current methods/main/solver.py on the practice fleet against its truth and prints per-device errors and the mean. The practice mean is not a point estimate of your qualification score. The 24-device practice fleet separates a broken method from a working one cleanly, but it is systematically optimistic about the qualification lot: for a strong method we have measured, the practice mean understates the qualification error by roughly a factor of 1.75. That gap is not sampling noise and a better method does not make it go away — the two fleets are different draws and the qualification lot leans toward the harder corner of the population, so part of the gap is priced in by construction. Use the practice mean for what it is good at: a floor check, a debugging tool, a regression guard, and relative comparisons between two of your own variants measured on the same 24 devices, where the fleet-specific offset largely cancels. Do not read an absolute practice number as the score you will get, and do not let a few percent of practice advantage outweigh physics you can justify.

What You Submit

Leave your best methods/main/solver.py (plus any helper files it needs inside methods/main/) in place. There is no submit step and no feedback from the graded lot: whatever sits in methods/main/ at the end is what the evaluation re-runs.

How It Is Judged

The evaluation re-runs your predict() once per qualification device, on records byte-identical to data/qualification/records.json, and compares your numbers to the sealed truth of that lot. Per device it computes the mean of |log10 I_pred − log10 I_true| over the device's extreme grid (LOWER is better; capped at 6.0, which is also the score for an invalid or over-budget run) — selfcheck.py computes the identical per-device error on practice. Device errors are averaged within each qualification condition (the main lot draw and the harder-corner draw from the same population), then across the two conditions. Your reward rises monotonically as that sealed mean error falls; at or above the shipped starting method's error it is zero.