Defect cluster recognition system for fabricated semiconductor wafers

Authors:
Melanie Po-Leen Ooi;Hong Kuan Sok;Ye Chow Kuang;Serge Demidenko;Chris Chan
Affiliations:
Monash University, Sunway Campus, Jalan Lagoon Selatan, Selangor, Malaysia;Monash University, Sunway Campus, Jalan Lagoon Selatan, Selangor, Malaysia;Monash University, Sunway Campus, Jalan Lagoon Selatan, Selangor, Malaysia;RMIT International University, 702 Nguyen Van Linh, Ho Chi Minh City, Vietnam;Freescale Semiconductor, Sungeiway Free Industrial Zone, Selangor, Malaysia
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

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Abstract

The International Technology Roadmap for Semiconductors (ITRS) identifies production test data as an essential element in improving design and technology in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to a systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying and categorising such clusters is a crucial step towards manufacturing yield improvement and implementation of real-time statistical process control. Addressing the semiconductor industry's needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type).