Using Statistical Process Control To Improve Yield and Traceability for Automated Production Test

About this Article
This article, jointly written by Steve Hughes, Ken Lamond and Jamie Mackay, was originally presented by Steve at the October 2002 meeting of ARMMS (the RF & Microwave Society).

• ARMMS

Abstract

Automated testing in Agilent has historically consisted of racks of measuring and test equipment (ATE systems) operated remotely by humans via software programs. In such an environment, certain tasks may still require manual intervention such as initiating test programs, making connections to the device under test and zeroing/calibrating power meters. Responding to wider demands for lower manufacturing overhead costs and increased production capacity, a fully automated production environment had already been designed and successfully implemented by a team in the Sonoma County division of Agilent, allowing 24/7 testing without human intervention. In principle, the migration of different production test processes to this automated environment at Agilent's Queensferry site was straight-forward, but a number of unforeseen problems conspired to reduce local operational performance to an unacceptable level. This paper describes how statistical process control (SPC) was employed to identify and overcome these problems, allowing almost continuous station operation with close to 100% process yield and traceability to national standards.

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SPC Improves Yield & Traceability