5 papers
LDU-Bench: Multimodal LLM Evaluation for Lithography Defect Understanding under Layout-Varying Circuit Backgrounds
Huanglong Ji, Botong Zhao, Shujing Lv +1
Multimodal large language models have demonstrated strong defect recognition capability in industrial anomaly detection. However, in lithography review, merely determining whether…
ProtoAnomalyNCD: Prototype Learning for Multi-class Novel Anomaly Discovery in Industrial Scenarios
Botong Zhao, Qijun Shi, Shujing Lyu +1
Existing industrial anomaly detection methods mainly determine whether an anomaly is present. However, real-world applications also require discovering and classifying multiple ano…
LithoSeg: A Coarse-to-Fine Framework for High-Precision Lithography Segmentation
Xinyu He, Botong Zhao, Bingbing Li +3
Accurate segmentation and measurement of lithography scanning electron microscope (SEM) images are crucial for ensuring precise process control, optimizing device performance, and…
Image-Intrinsic Priors for Integrated Circuit Defect Detection and Novel Class Discovery via Self-Supervised Learning
Botong. Zhao, Xubin. Wang, Shujing. Lyu +1
Integrated circuit manufacturing is highly complex, comprising hundreds of process steps. Defects can arise at any stage, causing yield loss and ultimately degrading product reliab…
Unsupervised Integrated-Circuit Defect Segmentation via Image-Intrinsic Normality
Botong Zhao, Qijun Shi, Shujing Lyu +1
Modern Integrated-Circuit(IC) manufacturing introduces diverse, fine-grained defects that depress yield and reliability. Most industrial defect segmentation compares a test image a…