English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Defect dies scattering on semiconductor wafer bin maps (WBM) tends to form specific patterns that point to particular manufacturing problems. The distribution of defect patterns from the shop floor is often highly imbalanced, leading to the challenge of having insufficient data about defect pattern types when building deep learning classification models. The method for completing such analysis in a timely manner with limited data is of critical interest. This study developed a method for applying content-based image retrieval (CBIR) and convolutional neural networking (CNN) to WBM defect patterns classification to solve the data imbalance problem and to improve accuracy when using relatively a small quantity of data. In this research, 3,600 WBMs featuring 12 defect pattern types were selected from the WM-811 K dataset for empirical validation. Using only 1,400 CNN training data elements, the overall classification accuracy reached 98.44%.

Integrating content-based image retrieval and deep learning to improve wafer bin map defect patterns classification