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Human Inspection of Defects in Wafer Map - Python for Integrated Circuits - - An Online Book - |
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| Python for Integrated Circuits http://www.globalsino.com/ICs/ | ||||||||
| Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= In the semiconductor industry, visible surface defects are mainly being inspected manually, which may result in inevitably erroneous classification. Many machine learning techniques-based recently pioneered arts in academia have been proposed to aid wafer failure pattern classification. In human wafer inspection, the inspectors randomly select samples from the total wafer population and try to find the root causes of defects by examining their locations, sizes, colors, and shapes using a high-resolution microscope. This human inspection procedure, however, is time consuming and highly subjective [1]. A previous study has shown agreement between observers to be as low as 45%, with long-term repeatability values of less than 95% [2]. ============================================
[1] F. L. Chen and S. F. Liu, “A neural-network approach to recognize defect
spatial pattern in semiconductor fabrication,” IEEE Trans. Semiconduct.
Manuf., vol. 13, no. 3, pp. 366–373, Aug. 2000.
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