When continuously improving, have you spent lots of time organizing data scattered all over the place for root causes finding?
How to identify variables of significance among the various variables,?
Without good SPC tools holistically monitoring in real time, how much unnecessary losses will cost?
Why are some data just unable to get into your SPC system?
AI has already become a popular doctrine, how to apply it to your production lines?
Do you expect higher accuracy in defect image classification to reduce the error rate caused by manual judgements?
As the IoT age, you must have deployed sensors to the machines, what is the most effective way to utilize such massive amounts of data?
A solution that can identify yield rate problems in the fastest manner—monitors all factors impacting quality and yield rates efficiently and accurately.
Quality parameter monitoring and anomaly management in the production process.
Deep learning and algorithms developed by TYNE can automatically detect defect positions and determine defect classification.
A comprehensive analysis system most suitable for the IC design industry and testing industry for test data integration.
Real-time machine anomaly monitoring allows fast yield rate improvement.
A smart analysis platform designated for organizing data from various sources, expanding analysis perspectives under the excellent efficiency of big data architecture.
TYNE ─ leading brand of industrial big data systems. At present, nearly 100 factories in the ICT industry use the products of TYNE.