Hybrid K -means clustering and support vector machine method for via and metal line detections in delayered IC images

• Main Authors: Cheng, Deruo, Shi, Yiqiong, Lin, Tong, Gwee, Bah-Hwee, Toh, Kar-Ann
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: IC Image Analysis; Support Vector Machine; Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/81439; http://hdl.handle.net/10220/50385
• Institution: Nanyang Technological University

This brief proposes a hybrid K-means clustering and support vector machine (HKCSVM) method to detect the positions of vias and metal lines from delayered IC images for subsequent netlist extraction. The main contributions of the proposed method include: 1) fully automated detection of via and metal line positions without any need of human interventions and 2) novel hybrid methodology to embody K-means clustering and support vector machine for retrieving precise positions of vias and metal lines in contrast to the individual techniques, which can only provide a region for the detected elements. From experiments on 50 SEM images of 1536 × 2048 pixels taken from fabricated IC chips (@TSMC 130-nm CMOS process), our proposed HKCSVM method achieves an F-score of 99.82% for via detection, and mean intersection over union of 94.44% and mean pixel accuracy of 95.83% for metal line detection, which are both superior to the reported approaches.