With the continuous shrinking of technology nodes, lithography hotspot detection and
elimination in the physical verification phase is of great value. Recently machine learning …

Detecting defects in the inspection stage of semiconductor manufacturing process is a
crucial task to improve yield and productivity as well as wafer quality. Recent Advances in …

Machine learning (ML) and pattern matching (PM) are powerful computer science
techniques which can derive knowledge from big data, and provide prediction and matching …

摘要模型性能表现和模型训练时间影响着基于迁移学习坏点检测方法的应用,
而选用模型和迁移学习策略是模型性能表现和模型训练时间的重要影响因素 …

As technology nodes continue to shrink, layout patterns become more sensitive to
lithography processes, resulting in lithography hotspots that need to be identified and …

Modern production lines for molded plastic parts often have automated inspection systems
to detect defective parts reliably and efficiently. However, these conventional inspection …

As the designed feature size of integrated circuits (ICs) continues to shrink, the lithographic
printability of the design has become one of the important issues in IC design and …

A small but diverse set of test patterns is essential for the optimization of lithography
parameters. We selectively extract the complicated patterns that are likely to cause …

At sub-22nm regime, chip designs have to go through hundreds to thousands of steps and
tasks before shipment. Many tasks are data and simulation intensive, thereby demanding …

The effective test pattern is a crucial component for lithography process optimization such as
Source Mask Optimization (SMO) and Optical Proximity Correction (OPC). The conventional …