Detecting and Scoring Equipment Faults in Real Time during Semiconductor Test Processes
Journal
IEEE Design and Test
Journal Volume
38
Journal Issue
4
Pages
119-126
Date Issued
2021
Author(s)
Abstract
This article proposes a robust calculation method for the identification of anomalies in IC manufacturing test data while eliminating the need of large storage of raw measurements. Having the capability to process live streaming data is now the fundamental requisite for the successful realization of industrial Internet of Things (IIoT). The presented framework supports the in-stream analysis of data by the unsupervised incremental binning (UIB) technique as shown in one of the algorithms. UIB groups individual values into a small but sufficient maximum number of bins, such as default maximum 100 bins, with each bin recording observed sum and count. Upon a newly received value, UIB creates a new bin with sum value and count 1.
Subjects
anomaly detection
collective anomaly
equipment faults
robust statistics
semiconductor test processes
Cost effectiveness
Costs
Fault detection
Cost effective
Level of use
Live streaming
Operational efficiencies
Real time
Semiconductor tests
Industrial internet of things (IIoT)
SDGs
Type
review