Improving the OEE and UPH data quality by Automated Data Collection for the semiconductor assembly industry

Tai Yue Wang, Hsiao Chun Pan

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Most semiconductor companies follow the SEMI E10 and E79 guidelines to measure equipment availability and efficiency via OEE (Overall Equipment Effectiveness). However, several problems need to be conquered before implementing OEE. For example, the time intervals of OEE losses are critical to the improvement studies, but it is very hard to collect reliable and accurate data. Therefore, people will find the unknown differences between the recorded losses from their operational system and OEE losses from SEMI E79 definition once people implement OEE. Furthermore, how to obtain the theoretical unit throughput or standard UPH (unit-per-hour machine rates) to determine the average processing rates of an equipment is another issue to be conquered. In this study, we develop an IT integrated system to record the time intervals of OEE losses for the bottleneck equipment "wire bonder" in the semiconductor assembly industry. We integrate a communication system (that is developed by the wire bonder supplier) and MES (Manufacturing Execution System) into an ADC (Automated Data Collection) system for collecting useful data. The data quality is further guaranteed by the real time detection of equipment status from the communication system. The application of the ADC system has eliminated the unknown OEE losses from original 6% of manual recording system to near zero percent. Therefore, the UPH data can be collected automatically once a production lot (partition of a specific batch in an order) is finished. This resolves the accuracy and timeliness problem associated with traditional stopwatch sampling.

Original languageEnglish
Pages (from-to)5764-5773
Number of pages10
JournalExpert Systems With Applications
Volume38
Issue number5
DOIs
Publication statusPublished - 2011 May 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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