Test Calculations

How to Calculate Chamber Time, HALT/HASS Capacity, and Failure Rate for a Reliability Lab

The core reliability-lab formulas worked line by line: cycle duration, humidity sample-hours, HALT and HASS capacity, and failure rate, with units and where each input comes from.

Every reliability-lab estimate starts with thermal cycle duration, because chamber hours drive everything downstream. Base time is planned cycles divided by achievable cycles per hour. A -40 to +125C profile with 15-minute dwells realistically runs 2.4 cycles per hour once representative DUT mass is loaded, so 240 cycles gives 100 base hours. Then multiply by one plus a setup, stabilization, and recovery allowance of 0.12 to reach 112 chamber hours. The Thermal Cycle Duration calculator does exactly this. Measure the cycle rate with product inside, not empty: heavy fixtures cut a chamber's nameplate 5C per minute ramp to under 3C per minute, and that single input error can shift the estimate 30% low.

Humidity jobs are dosed in sample-hours, not cycles, so the math changes shape. One sample-hour is one sample held at setpoint for one hour. An 85C/85%RH protocol requiring 250 samples at 48 hours each is 250 times 48, or 12,000 sample-hours. Divide by parallel throughput, meaning how many samples soak simultaneously: a chamber holding 48 delivers 48 sample-hours per clock hour, so 12,000 over 48 is 250 base hours. Apply an 8% stabilization and inspection allowance and total chamber time is 270 hours. The Humidity Exposure Time calculator handles the batch, not a single 48-hour dwell. Doubling fixture density to 96 sample-hours per hour halves base time to 125 hours if humidity stays uniform.

Fixture Loading converts a nominal fixture count into a defensible monitored run size. Gross positions are positions per fixture set times fixture sets: 16 times 4 is 64. But monitored tests need a live data channel per position, and setups fail. Multiply gross by instrumentation channel availability of 0.88 and setup yield of 0.95, so 64 times 0.88 times 0.95 is 53.5, meaning plan for 53 instrumented positions. Always round monitored capacity down, since you cannot run a fraction of a channel. The 0.88 channel factor alone removes 7.68 positions, so a thermocouple mux shortage, not fixture space, is often the real constraint on load size.

HALT capacity chains fixture count against two derating factors. Gross is usable samples per run times available runs: 12 times 10 is 120 samples per quarter. Multiply by HALT chamber availability of 0.86 and usable data yield of 0.92. That gives 120 times 0.86 times 0.92, or 94.9 usable samples. The loss breakdown matters as much as the total: 120 times (1 minus 0.86) is 16.8 samples lost to downtime, while 120 times 0.86 times (1 minus 0.92) is 8.3 lost to unusable data. The HALT Capacity calculator itemizes both so you attack the bigger drain, which here is availability, not data capture.

HASS capacity uses the same structure but in production units, on larger loads at reduced stress. Units per load times available loads sets gross: 36 times 22 is 792 units. Derate by chamber uptime of 0.90 and first-pass screen completion of 0.96, so 792 times 0.90 times 0.96 is 684.3 completed units. The downtime loss is 792 times 0.10, or 79.2 units, while reruns cost 792 times 0.90 times 0.04, about 28.5 units. HASS Capacity keeps HALT and HASS separate because a handful of destructively stressed samples and hundreds of screened units cannot share one throughput number without breaking your commit to production.

Failure rate estimation converts observed failures and accumulated test hours into a rate you can quote. Failure rate lambda equals failures divided by total device-hours. Run 40 units for 1,000 hours each with 2 failures and you have 80,000 device-hours, so lambda is 2 over 80,000, or 2.5e-5 per hour. Invert for MTBF: 40,000 hours. Express as FIT by multiplying lambda by 1e9, giving 25,000 FIT. The Failure Rate Estimate calculator does this arithmetic, but the load-bearing input is accumulated hours, not raw sample count, so twenty units run twice as long carry the same statistical weight as forty run once.

Queue lead time turns individual profile durations into a lab-level promise date. Lead time equals work-in-queue chamber hours divided by throughput chamber hours per day, plus the active job's own duration. If 640 chamber hours sit ahead of your 112-hour thermal job and the lab clears 96 chamber hours per day across its pool, queue wait is 640 over 96, about 6.7 days, then add the job itself. The Queue Lead Time calculator makes this explicit so you stop quoting from raw run time alone. A 112-hour profile is one number, but the customer feels the 6.7 days of queue in front of it far more sharply.

Two habits keep these calculations honest. First, feed every capacity formula measured values, not nameplate: loaded ramp rate for cycle duration, real channel counts for fixture loading, tracked uptime for HASS. A 90% assumed uptime that is actually 82% turns 684 committed units into 623, an 8.9% miss that shows up as a missed build gate. Second, keep base time and total time visible. When Thermal Cycle Duration shows a base-to-total ratio above 1.15, your setup and stabilization losses are excessive and worth attacking before you touch the profile. Pair these tools with Chamber Utilization to see how any single 112-hour job loads the month.

Published 2026-07-01.