Stepper Pattern Failure Map

Recently, deep learning methods are often used in wafer map failure pattern classification. CNN requires less feature engineering but still needs preprocessing, e.g., denoising and resizing.

Wafer maps can exhibit specific failure patterns that provide crucial details for assisting engineers in identifying the cause of wafer pattern failures. Conventional approaches of wafer map failure pattern recognition WMFPR and wafer map similarity ranking WMSR generally involve applying raw wafer map data i.e., without performing feature extraction. However, because increasingly more

To demonstrate the proposed prediction model, a dataset of a Unipolar Stepper Motor, from , has been used. The dataset used includes measurements collected from 32 unipolar stepper motors, which have been run simultaneously under 3 different environmental conditions, those being 10 C, 80 humidity. 20 C, 60 humidity. 50 C, 40 humidity

Wafer map failure pattern classication using geometric transformationinvariant convolutional neural network Iljoo Jeong1, SooYoung Lee1, Keonhyeok Park1, Iljeok Kim1, Hyunsuk Huh1 amp

Based on different kinds of detected wafer map failure patterns, it is possible to figure out the root causes of various process issues 1-4. The traditional visual recognition approach performed by an experienced person can be expensive and time-consuming. Therefore, investigating how to automate the wafer map failure pattern classification

The defect patterns, reflecting failure mechanisms, on wafer bin map are related to yield degradation. Fail bit maps FBMs represent the failed cell count during wafer functional probe tests and have been popularly used as one of the diagnosis tools in semiconductor manufacturing for process monitoring, root cause analysis, and yield improvements.

Wafer map defect pattern classification is essential in semiconductor manufacturing processes for increasing production yield and quality by providing key root-cause information. However, manual

The objective of this paper is to propose a systematic failure pattern recognition for wafer map based on neural networks. A deep convolutional neural network DCNN model which includes the convolutional layer, batch normalization layer, Relu layer, maximum pooling layer, full connection layer, Softmax layer and classification layer is established for the problem of failure pattern

In integrated circuit IC manufacturing, wafer-map analysis has been essential for yield improvement. In this study, we focused on wafer-map failure pattern recognition. We proposed a deep learning-based failure pattern recognition framework. The proposed framework needs only wafer-maps with and without target failure patterns to recognize, and ascertains the features of the target failure

Pattern Line Segment Arc Wafer Pattern Recognition customized search algo Lot Statistical Data Wafer Statistical Data Parametric Data Stack Maps Fail Bin Data INPUT J48 BP SVM Ensemble Hybrid NN Component Electrical Analysis