Spatial Pattern Recognition with Adjacency-Clustering: Improved Diagnostics for Semiconductor Wafer Bin Maps

by   Ahmed Aziz Ezzat, et al.

In semiconductor manufacturing, statistical quality control hinges on an effective analysis of wafer bin maps, wherein a key challenge is to detect how defective chips tend to spatially cluster on a wafer–a problem known as spatial pattern recognition. Detecting defect patterns on a wafer can deliver key diagnostics about the root causes of defects and assist production engineers in mitigating future failures. Recently, there has been a growing interest in mixed-type spatial pattern recognition–when multiple defect patterns, of different shapes, co-exist on the same wafer. Mixed-type spatial pattern recognition entails two central tasks: (1) spatial filtering, to distinguish systematic patterns from random noises; and (2) spatial clustering, to group the filtered patterns into distinct defect types. Observing that spatial filtering is instrumental to high-quality pattern recognition, we propose to use a graph-theoretic method called adjacency-clustering, which leverages spatial dependence among adjacent defective chips to effectively filter the raw wafer bin maps. Tested on real-world data and compared against a state-of-the-art approach, our proposed method achieves at least 49 terms of internal cluster validation quality (i.e., validation without external class labels), and about 6 external cluster validation metric based on external class labels. Interestingly, the margin of improvement appears to be a function of the defect pattern complexity, with larger gains achieved for more complex-shaped patterns. This superior performance is a testament to the method's promising impact to semiconductor manufacturing, as well as other contexts where mixed-type spatial patterns are prevalent.


page 2

page 4

page 13

page 15

page 16

page 22

page 23

page 30


Efficient Mixed-Type Wafer Defect Pattern Recognition Using Compact Deformable Convolutional Transformers

Manufacturing wafers is an intricate task involving thousands of steps. ...

A robust autoassociative memory with coupled networks of Kuramoto-type oscillators

Uncertain recognition success, unfavorable scaling of connection complex...

Validation of cluster analysis results on validation data: A systematic framework

Cluster analysis refers to a wide range of data analytic techniques for ...

Machine Learning based Indicators to Enhance Process Monitoring by Pattern Recognition

In industrial manufacturing, modern high-tech equipment delivers an incr...

Mixed-Type Wafer Classification For Low Memory Devices Using Knowledge Distillation

Manufacturing wafers is an intricate task involving thousands of steps. ...

Real time error detection in metal arc welding process using Artificial Neural Netwroks

Quality assurance in production line demands reliable weld joints. Human...

Please sign up or login with your details

Forgot password? Click here to reset