Recipe-Independent Indicator for Tool Health Diagnosis and Predictive Maintenance

Chen, A.; Blue, Jakey

doi:10.1109/tsm.2009.2028215

Cited by 37 publications

(11 citation statements)

References 10 publications

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“…1. Chen and Blue [20] suggest calculating the moving variance for a temporal series of observations to reduce the estimate bias. A similar idea may be used for the spatial data.…”

Section: Characterization Of Spatial Variationmentioning

confidence: 99%

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Spatial Variance Spectrum Analysis and Its Application to Unsupervised Detection of Systematic Wafer Spatial Variations

Blue

Chen

2011

IEEE Trans. Automat. Sci. Eng.

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Investigation of wafer spatial variations is critical for semiconductor process/equipment optimization and circuit design. The objective of spatial variation study is to differentiate the systematic variation component from the random component. This is usually done by contrasting with a set of known systematic patterns based on engineering knowledge. However, there could exist unknown systematic components remaining in the unexplained residuals and overlooked by the conventional spatial variation study. In this paper, we develop a novel spatial variance spectrum ( ) to analyze the systematic variations without any priori information of the systematic patterns. The is a series of spatial variations over a range of spatial moving window sizes from the smallest spatial moving window consisting of only two metrology sites to the largest one covering all metrology sites of the entire wafer. The can be used to characterize the wafer spatial variations and to detect existence of systematic variations by a proposed hypothesis test. We also propose an index to summarize from the the systematic proportion of the spatial variation. The proposed test and index of systematic variations will be demonstrated and validated through both hypothetical examples and actual cases of wafer critical dimension (CD) metrology data.Note to Practitioners-Wafer spatial variations study is critical to within-wafer control and optimization. Only through investigation of the spatial variations on the wafer surface do we get to learn more about the nature of the semiconductor process/equipment and the manufacturability of certain circuit design patterns. In this paper, we propose a novel analysis tool to help engineers characterize the wafer spatial variations and distinguish the systematic variation from the random variation. With the spatial systematic variations identified and described, the engineers can discover the opportunities to further improve the processing quality and thus the final yield.

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“…1. Chen and Blue [20] suggest calculating the moving variance for a temporal series of observations to reduce the estimate bias. A similar idea may be used for the spatial data.…”

Section: Characterization Of Spatial Variationmentioning

confidence: 99%

“…. (20) Given in (8), we have which is identical to (20). Each can be further expressed in terms of the sample variances of the samples with observations, and (20) becomes (21) where denotes the sample variance based on observations wherein the th and th observations are both excluded.…”

Section: Appendixmentioning

confidence: 99%

Spatial Variance Spectrum Analysis and Its Application to Unsupervised Detection of Systematic Wafer Spatial Variations

Blue

Chen

2011

IEEE Trans. Automat. Sci. Eng.

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“…Tool maintenance is conducted via monitoring of the machine or process performance with different level of complexity and efficiency. Process engineer conducts maintenance actions to improve uptime and availability and to reduce operational cost and scrap 2 . However, the process condition will change after the maintenance action.…”

Section: Introductionmentioning

confidence: 99%

A virtual metrology approach for maintenance compensation to improve yield in semiconductor manufacturing

Lin¹,

Hsu²,

Yu³

2014

IJCIS

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Process condition was changed by preventative maintenance that may lead to the inconsistency of process output. In practice, process engineers have difficulties to discover the inconsistency that the defect wafer may have been produced before the next measurement. This study proposes a virtual metrology (VM) approach for maintenance compensation in semiconductor manufacturing. The tool process parameter streams were collected to predict the product metrology value, and a relation model was built to adjust the equipment settings for product recipe compensation. An empirical study in maintenance compensation of chemical vapor deposition (CVD) was conducted in a Taiwan semiconductor company for validation. The thickness difference caused by maintenance was reduced from 571Å to 77Å in training data and 564Å to 210Å in testing data. The results showed practical viability of the proposed approach and an intelligent system embedded with the developed algorithm has been implemented.

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“…Following the concept of consolidating multiple FDC SVIDs into one single indicator based on GMV (Generalized Moving Variance) to monitor the tool health [6], Blue et al [7][8] propose a hierarchical tool condition monitoring scheme to more efficiently detect and diagnose tool faults. Fig.…”

Section: Introductionmentioning

confidence: 99%

FDC R2R variation monitoring for sensor level diagnosis in tool condition hierarchy

Blue

Roussy

Pinaton

2014

25th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC 2014)

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Tool behavior modeling and diagnosis is a big challenge in modern semiconductor fabrication, in particular for the foundry and analog companies with high product-mix and complicated technology nodes. Tool condition monitoring has been practiced by implementing the FDC (Fault Detection and Classification) system and analyzing large amount of real-time equipment data. This paper continues the work of tool condition hierarchy, where the excursions can be detected in one single overall tool indicator and are intuitively drilled down to the level of sensor groups. A R2R (Run-to-Run) variation monitoring technique is developed in order to correlate the tool faults with single sensor and thus completes the diagnostic gap of the hierarchy. The tool condition monitoring then becomes efficient and tool fault diagnosis can be systematically topdown.

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Recipe-Independent Indicator for Tool Health Diagnosis and Predictive Maintenance

Cited by 37 publications

References 10 publications

Spatial Variance Spectrum Analysis and Its Application to Unsupervised Detection of Systematic Wafer Spatial Variations

Spatial Variance Spectrum Analysis and Its Application to Unsupervised Detection of Systematic Wafer Spatial Variations

A virtual metrology approach for maintenance compensation to improve yield in semiconductor manufacturing

FDC R2R variation monitoring for sensor level diagnosis in tool condition hierarchy

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