Bayesian network for integrated circuit testing probe card fault diagnosis and troubleshooting to empower Industry 3.5 s
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Bayesian network for integrated circuit testing probe card fault diagnosis and troubleshooting to empower Industry 3.5 smart production and an empirical study Wenhan Fu1 · Chen-Fu Chien1,2
· Lizhen Tang1
Accepted: 24 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Probe card that serves as the carrier of die and the transmitter of information is an indispensable test interface for integrated circuit testing. The probe card extracts the electrical signal of chip and sends it to the prober to screen defectives. In the process of interface transmission, signal disturbance or attenuation will lead to functional errors, and the output result will be different from the expected one to the screen of selected known good dies for integrated circuit packaging and final test. If abnormal situations happen with probe cards, the engineers will eliminate potential fault causes by trial and error method according to domain knowledge and personal experience for troubleshooting. As semiconductor industry is continuously migrating with shrinking feature size, the diagnosing and troubleshooting procedure for probe card is exponentially complicated and time-consuming. To enhance data integrity of circuit probe testing, this study aims to develop a Bayesian network for probe card fault diagnosis and troubleshooting via the integrated data-driven solutions considering potential rules derived from domain knowledge and manufacturing big data to empower Industry 3.5 smart production. An empirical study is conducted in a leading semiconductor testing company for validation. The experiment results show that the proposed approach can improve one-shot probability from 0.13 to 0.36 to improve one-shot success chance in troubleshooting process. The expected shooting times for probe faults have been reduced from 11 times to 3.96 times on average to save 63.97% of fault troubleshooting efforts in testing process. The proposed approach can provide effective suggestions to shorten troubleshooting time for yield improvement and subsequent packaging cost reduction to empower flexible decision-making for smart production. The results have shown practical viability of proposed approach for Industry 3.5. Indeed, the developed solutions have been implemented in real settings. Keywords Industry 3.5 · Bayesian network · Advanced quality control · Fault diagnosis · UNISON decision framework
Introduction Semiconductor manufacturing is capacity-intensive with expensive production equipment, complicate manufacturing processes, high customer response requirements, and demand uncertainty (Chien et al. 2007, 2018). The enterprises in semiconductor supply chain have to improve
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Chen-Fu Chien [email protected]
1
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101 Sect. 2 Kuang Fu Road, 30013 Hsinchu, Taiwan
2
Artificial Intelligence for Intelligent Manufacturing Systems (AIMS) Research Center, Ministry of Science and Technology, 30013 Hsinchu, Taiwan
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