Abstract:
Integral to author's own PhD research is the investigation on the enhancements to variability in semiconductor industry Automatic Test Equipment's (ATE's) equipment maintenance time. Based on industry's case studies on product yields, ATE downtime, and ATE throughput time, ATE process variability is perceived as a real problem in semiconductor manufacturing industry. However, effective methods for addressing process variability is not available in the literature. The author proposed a relationship-based research where Independent variables (IV) designated as Production Time (PT), Idling Time (IT), Repair Time (RT), and Engineering Time (ET) with Production Yield (PY) as dependent variable (DV) are being used. A chaos theory four-quadrant phase space was plotted with coordinates in a chronological order. Xaxis represents “PT changes”, “IT changes”, “RT changes” and “ET changes” signifying differences in factory shift ATE's time, while “PY changes” illustrated differences in output on y-axis in separate charts. Quadrant in the upper-right section embodies increase in factory output with increase in ATE's IV and DV. Quadrant in the lower-right section denotes ATE's increase in IV but decrease in DV where ATE participates in unproductive work. Quadrant in the upper-left section symbolizes decrease in IV but increase in DV in consequence of factory improvement activities. Quadrant in the lower-left section illustrates both decrease in IV and DV proving that ATE is shut down. Analysis shows positive linear PT-PY, negative linear ET-PY, while both IT-PY and RT-PY graphs as extremely erratic. Judging on the results, cumulative ATE time characteristics can be comprehended, which provide some clarity in predictable equipment performance for support maintenance prioritization and task management, and for future research directions on prediction capability for equipment capacity improvement. In conclusion, the chaos theory's phase space diagrams were successfully applied to simulate the chaotic characteristics and unpredictability in equipment performance in guiding maintenance teams to better prioritize maintenance tasks management, and for future research directions, to enable better prediction on equipment capacity improvement.
Page(s):
5308-5317
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 16, Year: 2022
Keywords:
PhaseSpace Diagrams
,
Chaos Theory
,
Modeling Techniques
,
manufacturing