Temporal and Spatial Variation Analysis and Its Applications to Semiconductor Engineering Data Analysis
Date Issued
2010
Date
2010
Author(s)
Blue, Jakey
Abstract
Investigation of system variation is always critical to process/equipment optimization and yield enhancement in semiconductor manufacturing. Conventional variation estimate, usually the sample variance, cannot truthfully reveal the random variation if data exhibits a patterned profile or is of non-stationary distribution. The biased random variation estimate could then impact the subsequent analysis greatly. In this research, the concept of moving variance, which calculates the variance of a small number of consecutive/adjacent observations within a temporal/spatial moving window, is proposed to eliminate the impact of the pattern-induced (systematic) variation. By applying the moving variance technique to temporal profiles, such as the process states or tool signals, the tool condition can be evaluated by the proposed tool condition indicator. When dealing with spatial topography, such as the wafer metrology data, systematic variations can be identified and characterized by the proposed spatial variation spectrum (SVS) comprised of the spatial moving variances. Diagnosis methodologies are developed to facilitate uncovering abnormal tool conditions or systematic patterns. Properties and theories are studied as well to justify how the moving variance outperforms the conventional sample variance. With the tool condition indicator, possible tool faults can be identified and proper maintenance measures can be scheduled accordingly. With the SVS and its summarized indices, systematic variations can be characterized and the causal analysis for finding root causes can be further explored. The proposed methodologies are further validated through the real cases provided by local semiconductor companies.
Subjects
fault detection and classification (FDC)
moving variance
tool condition indicator
spatial variation
systematic variation
random variation
wafer CD metrology
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