关键词: 超维计算, 未知缺陷检测, 分布外检测, 特征融合, 损失函数, 位置编码, 晶圆缺陷

Abstract: Since existing methods for wafer map unknown defect detection require new methods to retrain models, making it difficult to reuse existing classification models, a post-hoc unknown defect detection method based on Hyperdimensional Computing (HDC) was proposed. Without modifying the existing classification model, a Hyperdimensional Positional Binding-Rotate Fusion (HPBRF) module and a class hypervector retraining module based on HDC Cosine Similarity Loss (HCosineLoss) were introduced. First, position encoding vectors were generated for multiple layers of features and embedded into feature vectors in a hyperdimensional space through a binding-rotate operation. Secondly, the hypervectors of each layer were fused via a bundling operation to obtain a more informative feature hypervector. Finally, class hypervectors were retrained using HCosineLoss with the fused feature hypervectors to align each class hypervector with its corresponding feature hypervector and separate different class hypervectors. Experiments on the WM811K dataset show that the proposed method improves the average AUROC (Area Under the Receiver Operating Characteristic Curve) by 12.52, 11.70, 6.71, and 6.58 percentage points compared to ASH (Activation SHaping), LTS (LogiT Scaling), KNN (K-Nearest Neighbors), and Gram methods, respectively. Efficiency analysis shows that the average processing time is 0.27 ms, the peak memory usage is 461.82 MB, and the additional storage overhead is only 36 KB. The proposed method improves the detection capability of unknown defects through posterior ensemble with low additional computational and storage overhead.

Key words: HyperDimensional Computing (HDC), unknown defect detection, out-of-distribution detection, feature fusion, loss function, position encoding, wafer map defects