摘要
Despite significant advancements in deep classification techniques and in-laboratory automated optical inspection (AOI) models for long-tailed or highly imbalanced data, applying these approaches to real-world IC defect classification tasks remains challenging. This difficulty stems from two primary factors. First, real-world conditions, such as the high yield-rate requirements in the IC industry, result in data distributions that are far more skewed than those found in general public imbalanced datasets. Consequently, classifiers designed for open imbalanced datasets often fail to perform effectively in real-world scenarios. Second, real-world samples exhibit a mix of class-specific attributes (e.g., defect types) and class-agnostic, domain-related features (e.g., design characteristics of product lines). This complexity adds significant difficulty to the classification process, particularly for highly imbalanced datasets. To address these challenges, this article introduces the IC-Defect-14 dataset, a large, highly imbalanced IC defect image dataset sourced from AOI systems deployed in real-world IC production lines. This dataset is characterized by its unique "intraclass clusters" property, which presents two major challenges: large intraclass diversity and high interclass similarity. These characteristics, rarely found simultaneously in existing public datasets, significantly degrade the performance of current state-of-the-art (SOTA) classifiers for highly imbalanced data. To tackle this challenge, we propose the regional channel attention-based multiexpert network (ReCAME-Net). This network follows a multiexpert classifier framework and integrates a regional channel attention module, metric learning losses, a hard category mining strategy, and a knowledge distillation procedure. Extensive experimental evaluations demonstrate that ReCAME-Net outperforms previous SOTA models on the IC-Defect-14 dataset while maintaining comparable performance and competitiveness on general public datasets. Our resources can be found at https://github.com/YoursEver/ReCAME-Net