CHAM: weak signals detection through a new multivariate algorithm for process control

François Bergeret; Carole Soual; B. Le Gratiet

doi:10.1117/12.2248737

20 October 2016 CHAM: weak signals detection through a new multivariate algorithm for process control

François Bergeret, Carole Soual, B. Le Gratiet

Proceedings Volume 10032, 32nd European Mask and Lithography Conference; 100320J (2016) https://doi.org/10.1117/12.2248737
Event: 32nd European Mask and Lithography Conference, 2016, Dresden, Germany

Abstract

Derivatives technologies based on core CMOS processes are significantly aggressive in term of design rules and process control requirements. Process control plan is a derived from Process Assumption (PA) calculations which result in a design rule based on known process variability capabilities, taking into account enough margin to be safe not only for yield but especially for reliability. Even though process assumptions are calculated with a 4 sigma known process capability margin, efficient and competitive designs are challenging the process especially for derivatives technologies in 40 and 28nm nodes. For wafer fab process control, PA are declined in monovariate (layer1 CD, layer2 CD, layer2 to layer1 overlay, layer3 CD etc….) control charts with appropriated specifications and control limits which all together are securing the silicon. This is so far working fine but such system is not really sensitive to weak signals coming from interactions of multiple key parameters (high layer2 CD combined with high layer3 CD as an example). CHAM is a software using an advanced statistical algorithm specifically designed to detect small signals, especially when there are many parameters to control and when the parameters can interact to create yield issues. In this presentation we will first present the CHAM algorithm, then the case-study on critical dimensions, with the results, and we will conclude on future work. This partnership between Ippon and STM is part of E450LMDAP, European project dedicated to metrology and lithography development for future technology nodes, especially 10nm.

Citation Download Citation

François Bergeret, Carole Soual, and B. Le Gratiet "CHAM: weak signals detection through a new multivariate algorithm for process control", Proc. SPIE 10032, 32nd European Mask and Lithography Conference, 100320J (20 October 2016); https://doi.org/10.1117/12.2248737

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KEYWORDS

Process control

Critical dimension metrology

Signal detection

Semiconducting wafers

Signal processing

Lithography

Machine learning

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