Conference Paper

Multilevel statistical process control of asynchronous multi-stream processes in semiconductor manufacturing.

Univ. of Pavia, Pavia, Italy
DOI: 10.1109/COASE.2010.5584508 Conference: IEEE Conference on Automation Science and Engineering, CASE 2010, Toronto, ON, Canada, 21-24 August, 2010
Source: DBLP

ABSTRACT In semiconductor manufacturing, the purpose of chamber matching is the alignment of process and yield results of distinct chambers performing in parallel the same process step on different silicon wafers. In this paper, multi-level linear models and statistical process control techniques are jointly employed to define control charts for monitoring chamber matching accuracy and preemptively report chamber misalignments. Specifically, multilevel versions of the classic T2 Control Chart, MEWMA Control Chart and Self-Starting Control Chart are defined and tested against experimental and simulated data.

0 Followers
 · 
93 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: In semiconductor manufacturing, metrology operations are expensive and time-consuming, for this reason only a certain sample of wafers is measured. With the need of highly reliable processes, the semiconductor industry aims at developing methodologies covering the gap of missing metrology information. Virtual Metrology turns out to be a promising method; it aims at predicting wafer and/or site fine metrology results in real time and free of costs. In this paper, we present a sampling decision system that relies on virtual measurements suggesting an efficient strategy for measuring productive wafers. Several methods for evaluating when a real measurement is needed (including the expected utility of measurement information, a two-stage sampling decision model and wafer quality risk values) are proposed. We further provide ideas on how to assess and update the reliability of the virtual measurements in a sampling decision system (whenever real measurements become available). In this context, we introduce equipment health factors and virtual trust factors for improving the reliability of the sampling decision system. Finally, the performance of the sampling decision system is demonstrated on a set of virtual and real metrology data from the semiconductor industry. It is shown that wafer measurements are efficiently performed when really needed.
    IEEE Transactions on Automation Science and Engineering 12/2014; 12(1):75-83. DOI:10.1109/TASE.2014.2360214 · 2.16 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: 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 top-down.
    2014 25th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC); 05/2014
  • [Show abstract] [Hide abstract]
    ABSTRACT: Tool condition evaluation and prognosis has been an arduous challenge in the modern semiconductor manufacturing environment. More and more embedded and external sensors are installed to capture the genuine tool status for fault identification. Therefore, tool condition analysis based on real-time equipment data becomes not only promising but also more complex with the rapidly increased number of sensors. In this paper, the idea of generalized moving variance (GMV) is employed to consolidate the pure variations within tool fault detection and classification data into one single indicator. A hierarchical monitoring scheme is developed to generate an overall tool indicator that can coherently be drilled down into the GMVs within functional sensor groups. Therefore, we will be able to classify excursions found in the overall tool condition into sensor groups and make tool fault detection and identification more efficient.
    IEEE Transactions on Semiconductor Manufacturing 02/2013; 26(1):82-91. DOI:10.1109/TSM.2012.2230279 · 0.98 Impact Factor