Clustering the dominant defective patterns in semiconductor wafer maps

Identifying defect patterns on wafers is crucial for understanding the root causes and for attributing such patterns to specific steps in the fabrication process. We propose in this paper a system called DDPfinder that clusters the patterns of defective chips on wafers based on their spatial dependence across wafer maps. Such clustering enables the identification of the dominant defect patterns. DDPfinder clusters chip defects based on how dominant are their spatial patterns across all wafer maps. A chip defect is considered dominant, if: (1) it has a systematic defect pattern arising from a specific assignable cause, and (2) it displays spatial dependence across a larger number of wafer maps when compared with other defects. The spatial dependence of a chip defect is determined based on the contiguity ratio of the defect pattern across wafer maps. DDPfinder uses the dominant chip defects to serve as seeds for clustering the patterns of defective chips. This clustering procedure allows process engineers to prioritize their investigation of chip defects based on the dominance status of their clusters. It allows them to pay more attention to the ongoing manufacturing processes that caused the dominant defects. We evaluated the quality and performance of DDPfinder by comparing it experimentally with eight existing clustering models. Results showed marked improvement

A review of data mining applications in semiconductor manufacturing

The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.publishersversionpublishe

Modelo de previsão baseado em agrupamento e base de regras nebulosas

Orientador: Fernando Antonio Campos GomideDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoMestrad

Desarrollo de una metodología para la aplicación de la minería de datos en el diseño estructurado de circuitos integrados a muy gran escala (VLSI).

Proyecto de Graduación (Maestría en Computación con énfasis en Ciencias de la Computación). Instituto Tecnológico de Costa Rica. Escuela de Ingeniería en Computación, 2014.We live in a world in which a huge amount of data is generated on a daily basis, where even the amount of data generated daily follows an increasing trend. However, our analysis capabilities are limited, preventing us from analyzing all this data in order to use it to improve our decision making process. This problem is present in many areas of the human endeavor, one of them being the design of very large scale integrated circuits. Precisely in this area, the idea of creating a methodology to develop data mining systems optimized for a specific area is born. That methodology is described, step by step, in this thesis. It is then applied in the area of design of very large scale integrated circuits. The results are explained in detail, demonstrating the effectiveness not only of the methodology, but also of applying data mining techniques to improve the decision making process. The document ends with a list of recommendations to improve the data mining system that was developed. This, through training programs as well as tool and database enhancements.Instituto Tecnológico de Costa Rica. Escuela de Ingeniería en Computación

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes