Taipei, Wednesday, Apr 24, 2024, 07:30

News

Big Data Helps TSMC in Improving Defect-free Rate

By Vincent Wang
Published: Nov 20,2014

TAIPEI, Taiwan — The Ministry of Science and Technology (MOST) illustrated how big data is going to play an important role in improving the defect-free rate of wafer production at the early test stage. The industry-academic cooperation findings could lower the defect rate 14 percent of TSMC's wafer production at the early test stage.

More on This

Taiwan’s ARRC Accomplishes Its First Rocket Launch in Pingtung County

PINGTUNG, Taiwan - The Pingtung County’s scientific research rocket launch site in Peony Township hold its first launc...

ITRI and TSMC Announces a New SOT-MRAM Technology at VLSI 2020

TAIPEI, Taiwan – Taiwan’s ITRI(Industrial Technology Research Institute) announced a world-leading SOT-MRAM technology which is co-developed with TSMC...

The Deputy Minister of MOST Chien Chung-liang emphasized that this findings showing TSMC's defect rate lowered 11 percent to 14 percent which means wafer production at the early stage, not that of at the mass production stage. “I hope that this findings won't confuse you all, becasue the defect-free rate of TSMC is very high.”

Chien Chung-liang said that in the 2016 MOST R&D program has highlighted big data, echoing Intel and TSMC tried to find solutions out of big data lately.

This big data research team is led by the research fellow of Institute of Statistical Science Academia Sinica Ing Ching-kang, he stated that the manufacturing process of wafer is complicated for in which needed to be painted layer by layer. “It's extremely important for those wafer developers to find out the key machines at the early test stage to improve the defect-free rate. Big data is the way to find out that key machines.”

“If one wafer is needed to pass through 300 machines, generally speaking, to find out the problem machine is 2 to the power of 300, which is definitely a big data.” Ing added.

Ing Ching-kang gave a clearly example on how big data is working in this field. Firstly, ranking the machines in terms of performance based on statistics. Secondly, setting the truncation point by probability. Thirdly, examining to find out the problem machines.

CTIMES loves to interact with the global technology related companies and individuals, you can deliver your products information or share industrial intelligence. Please email us to en@ctimes.com.tw

3171 viewed

Most Popular

comments powered by Disqus