Adaptive test

Notes

Attractive because it improves quality and reduces test time at once, but it demands a robust data pipeline and careful traceability.

Overview

Adaptive test is the practice of changing a semiconductor test programme while it runs, adjusting test content, test conditions or pass and fail limits for each device, wafer or lot based on data already collected. The aim is to give every part the tests it actually needs: dropping tests that never fail to save time, and adding or tightening tests for parts that look marginal to protect quality. It contrasts with a fixed test programme that applies the same sequence and static limits to every device.

How it works

Adaptive decisions draw on several data sources: results from earlier tests on the same device, the behaviour of its neighbours on the wafer, data from earlier manufacturing steps such as inline inspection and wafer sort, and readings from on-die sensors and monitors. Rules or statistical and machine learning models then decide what happens next.

Typical actions include removing or sampling tests that have not failed across a large population, switching a high-yielding product from testing every die to sample testing, adding extra stress or retest for devices whose measurements sit near a limit, and recalculating limits from the population being tested rather than using fixed values. The last idea is closely related to part average testing, which flags parts that are statistical outliers even though they pass the specification.

Decisions are made at two speeds. Offline analysis of collected data adjusts the programme for future lots. Real-time adaptive test makes decisions while the device is still in the tester, which needs low-latency data infrastructure and computing in or next to the test cell; tester vendors and analytics suppliers now offer platforms for running such models directly at the test cell.

The risks are escapes and overkill. A model that skips the wrong test lets bad parts through; one that is too cautious scraps good parts. Test cell variation from sockets, probe cards and load boards can also mislead models, so data quality and careful validation come first.

Examples

• Removing a group of tests from a mature product's flow after they have not failed across many lots, then adding them back if failures reappear.
• Switching a high-yield wafer from testing every die to sample testing based on early results from that wafer.
• Running a machine learning model inside the test cell that flags a die for extra screening because its parametric results drift from its neighbours.
• A two-pass test flow that retests failing parts only when the expected recovery yield justifies the tester time.

Related terms

• PAT
• Outlier screening
• STDF
• Cost of test

Where it appears on the Hub

Used on 7 pages of the Hub. In each group, the pages that mention it most come first.