Low‐Cost High‐Accuracy Variation Characterization for Nanoscale IC Technologies via Novel Learningbased Techniques
Zhijian Pan1, Miao Li2, Jian Yao2, Hong Lu2, Zuochang Ye1, Yanfeng Li2 and Yan Wang1,a
1Institute of Microelectronics, Tsinghua University, Beijing 100084, China
awangy46@tsinghua.edu.cn
2Platform Design Automation, Inc., Cameo Center, Beijing 100102, China
albert_li@platform-da.com
ABSTRACT
Faster and more accurate variation characterizations of semiconductor devices/circuits are in great demand as process technologies scale down to Fin‐FET era. Traditional methods with intensive data testing are extremely costly. In this paper, we propose a novel learning‐based high‐accuracy data prediction framework inspired by learning methods from computer vision to efficiently characterize variabilities of device/circuit behaviors induced by manufacturing process variations. The key idea is to adaptively learn the underlying data pattern among data with variations from a small set of already obtained data and utilize it to accurately predict the unmeasured data with minimum physical measurement cost. To realize this idea, novel regression modeling techniques based on Gaussian process regression and partial least squares regression with feature extraction and matching are developed. We applied our approach to real‐time variation characterization for transistors with multiple geometries from a foundry 28nm CMOS process. The results show that the framework achieves about 14x time speed‐up with on average 0.1% error for variation data prediction and under 0.3% error for statistical extraction compared to traditional physical measurements, which demonstrates the efficacy of the framework for accurate and fast variation analysis and statistical modeling.
