William Zhou
Consulting Director, Semiconductor Industry, JMP China

Biography

  • Quality management analyst
  • Senior professional member, China Association for China
  • Master’s tutor (part-time), School of Statistics & Management, Shanghai University of Finance & Economy
  • Master of Technical Economics & Management, Bachelor of Electronics
  • Worked in Siemens Communication BU for a long time
  • Proficient in the concept and practice of digital management, good at the wide application of EDA, DOE, Big Data, Modeling & Prediction, Reliability, SPC, MSA, Marketing Research and other analytical methods in reality.
  • Invited writer of “Quality”, a magazine from China Association for China. Published a series of professional papers in “China Quality”, “CAD/CAM & Manufacture Informationization”, “International Journal of Metrology and Quality Engineering”, and so on.
  • Wrote and published "Six Sigma Statistical Guide", a reference book which China Association for Quality recommended
  • Accumulate rich experience in the field of wafer manufacture, assembly & test, equipment and material
  • Provide professional training and coaching service to many well-known companies and universities, such as Apple, Microsoft, Huawei, Lenovo, Seagate, SanDisk, AMD, Freescale, Fairchild, TSMC, SMIC, ST, Amkor, ASE, Applied Material, etc.


Abstract

With the great supporting & huge investment from government, China semiconductor industry has been in a period of rapid development. But it is a pity that most enterprises either tried to avoid data, or were lost in data jungle during their way of improving the degree of automation, reducing the cost of poor quality, which lead to a large waste of resources and low efficiency. In fact, the modern, mature information technology has paved the way for the intelligent development of high-tech manufacturing, semiconductor industry is one of the most suitable industry to achieve the goal of Industry 4.0.

The speaker will share with you a smart manufacturing lifting scheme based on industrial data, introduce data visualization, interactive analysis, text mining, custom experimental design and other new data analysis technology in industrial application environment, help you recognize the potential value of big data, improve the degree of production automation, realize that big data analysis is available, build up confidence on making data-driven decision. All of these are beneficial for enterprises to take the lead in a new round of manufacture technology innovation competition.