Zhang Yaowei
Director of QA Department
SanDisk Semiconductor (Shanghai) Co. Ltd., Western Digital Technologies, Inc.
Speech Topic:
Big Data for Commonality Analysis and Quality Prediction of Semiconductor Manufacturing
Abstract:
In the manufacturing industry, we have focused on process fluctuation. What is process variation? What kind of variation may lead to quality problems? Which kinds of fluctuations may cause product failure? As the manufacturing process of semiconductor is so sophisticated, what may happen if variations in the former and the subsequent process overlap? Against a factory applying big data, manufacturing of wafers, packaging wafers, and testing finished products, realizes the serious connection, as well as business applications. All of this will be introduced. To identify key parameters from dozens, hundreds or even thousands of input variables and properly control them so as to strike a balance between quality and costs is the most difficult and critical problem of the sophisticated process flow. Therefore, commonality analysis will help both diagnose and predict such aspects. To conduct commonality analysis, constant trials and verifications have to be carried out throughout all the links from data cleansing, data connection, algorithm selection to result analysis. To achieve this purpose, we have chosen JMP software to empower the process. More details will be provided in this talk.
Background:
Zhang Yaowei is an expert with nearly 20 years’ experience in the semiconductor industry. With work experience in Intel, STATS ChipPAC, and Microsoft, Zhang is currently Director of QA Department of SanDisk Semiconductor (Shanghai) Co. Ltd., Western Digital Technologies, Inc. He has unique insights in semiconductor research and development, process manufacturing and quality management. As an adherent to innovation, Zhang keeps leading his team in practicing innovative thinking and creating great benefits for the company. His valuable experience has become not only the wealth of the company, but also a driving force of the whole industry for improvement. As the special tool for data analysis in the manufacturing industry, JMP software was brought by Zhang from Intel to STATS ChipPAC and SanDisk, and a large number of professionals were trained.
Big Data Mining in IC Manufacturing: Defect Prediction Model
In 2016, AlphaGo defeated the top Go players one by one. And in 2017, AlphaGo Zero beat all its predecessors through self-learning without human experience. Since then, humans entered the first year of AI. With the continuous development of information technology, the application of data, especially the application of big data in industry, plays an increasingly important role in the manufacturing industry. Integrated circuit (IC) manufacturing is a high-tech-intensive industry. The products’ very small dimensions and high-precision requirements are doomed to have a very close relationship with data analysis. This talk mainly introduces how to use the real-time equipment monitoring data in IC production to find the root cause of defects with data mining and machine learning methods to achieve quality improvement, cost reduction and productivity enhancement. Let’s see the powers and prospects of AI in the industry. We’ll share some actual cases to introduce how to use the model predictive function of JMP in defect prediction and virtual metrology of WAT based on IC manufacturing data. We hope that these cases will inspire.
Kang Sheng graduated with a master’s degree from East China Normal University, specializing in probability and mathematical statistics. He now works at Semiconductor Manufacturing International Corp. and serves as Senior Manager of the Quality and Reliability Center. He has 13 years of semiconductor manufacturing experience, with expertise in quality management, statistical process control (SPC), and design of experiments (DOE), etc. On behalf of the company, he has made outstanding contributions to the industrial internet and industrial big data analysis, through participation in the establishment of the Alliance of the Industrial Internet Shanghai Branch. He has more than 15 published papers and 19 patents granted in the US and China in the areas of industrial statistics and semiconductor quality and reliability.
Thomas Chen Director of Product Engineering and Test Engineering
Yangtze Memory Technologies
Topic：Data Analysis in NAND Flash Development and Testing
Abstract:
When we get into to the age of big data, we look at two important indictors to measure how deep to get involved in this revolution – memory size and network bandwidth.
About 20 years ago, was a time when most people in the high-tech industry were still talking about the “computing capability” and were excited about the releases of Pentium serials. Today, only memory size, 128GB, 256GB …, differentiates an iPhone price. People are now more interested in how data is stored and transferred.
Memory, especially NAND flash, is widely utilized in an individual’s daily life. From computers to cell phones, from data centers to enterprise grade servers, NAND flash has helped different applications solve data storage problems with higher efficiency, higher security and lower power. At the same time, big data applications also drive the memory industry, making it the first segment in semiconductor, occupying a third of the total semiconductor market.
YMTC is the first and only IDM Company in China to develop and manufacture NAND flash. Given the capacity target of YMTC, which is over 300K wafers per month, over 1020 transistors will be tested every month in YMTC’s factory. Accurate and efficient data analysis is one of the most important ways to help quickly develop new products, stabilize mass production line and improve yield, quality and reliability.
A couple of examples will be introduced during the presentation on how JMP is utilized during YMTC’s product development stage, and how it can help in future mass production.
本次演示将使用几组实例，说明如何通过JMP软件辅助长江存储的产品研发及未来量产。
Introduction:
Thomas Chen (陈轶), graduated from Fudan University and received a bachelor’s degree in theoretical physics and a master’s degree in electrical engineering. Chen joined Intel in 2001 towork on NOR flash product developments. In early 2010, he joined Micron and started work on NAND flash product developments. Chen joined Yangtze Memory Technologies in early 2016 as Director of Product Engineering and Test Engineering. Over the past 16 years of his career, Chen has participated in over 10 generations of flash product R&D, starting from 230nm up to 64 layer 3D NAND flash.
Voca Lai
Senior Data Scientist
R&D Manager, Taiwan's Semiconductor Assembly & Testing Giant
Topic：
The Standardization of Big Data Analysis and JMP® Experimental Flow
Abstract：
The recent upsurge of big data is still hot, but most of its applications in the market is limited to summaries or regression and does not include analysis. Without analysis, we cannot do further exploration of root causes and then make decisions or make predictions. The general application of big data is incomplete, and people should not fall to its magical charms. This lecture, based on the author’s practical experience in the semiconductor field for many years, will show you the data analysis steps with a standardized flow from sorting to analysis for decision making or making predictions and then solving the problem. The unique tools in JMP provide a template for a perfect demonstration.
Introduction：
Voca Lai, mechanical engineering master, graduated from National Chiao Tung University (NCTU) in Taiwan and National Chung Hsing University (NCHU) in Taiwan. He is also a member of Chinese Society for Quality (CSQ) in Taiwan and a Certificated Quality Engineer (CQE). He works at Taiwan’s semiconductor assembly and testing giant as an R&D manager and the chief representative of statistical training and statistical consulting. He has contributed to integrated circuit technology’s development and improvement for more than 20 years, with experience in R&D, QA, ENG, MFG and PD.
Multivariate Analysis of Sensory and Consumer Data With JMP®
Jianfeng Ding, JMP Senior Research Statistician Developer, SAS
Data sets resulting from sensory and consumer studies can be quite large, with many different columns and data types. A variety of multivariate data analysis methods can be useful in the exploration and analysis of sensory data. Over the past few years, many of these methods have been added to JMP. In this paper, we present how to apply methods such as analysis of variance (ANOVA), K means cluster, principal components analysis, partial least squares, multiple factor analysis, multiple correspondence analysis and Text Explorer to sensory and consumer data, emphasizing how each of these procedures operates, how each is interpreted, and how they relate to one another. By illustrating the best methods to address sensory and consumer preference problems, the goal of this paper is to familiarize analysts with sensory evaluation, consumers’ preferences and appropriate multivariate methods so that each analyst can effectively use these methods in JMP for their own sensory studies.
Jianfeng Ding
JMP Senior Research Statistician Developer, SAS
Jianfeng Ding joined SAS as a statistical tester in 1998 and tested a lot of platforms within JMP. She became a statistician developer in 2004. Since then she has developed and been responsible for many statistical platforms such as Multivariate, Principal Components, Factor Analysis, Partial Least Squares and Multiple Correspondence Analysis. Ding earned a master’s degree in marine, earth and atmospheric sciences, and a master’s degree in statistics from North Carolina State University.
Xun Liu
Vice President, Shanghai Hengrui Pharmaceuticals
Chief Scientific Officer of Suncadia Biopharmaceuticals, a subsidiary of Hengrui Medicine
Head of Shanghai Hengrui Biopharmaceutical Research Institute
Efficient and Effective Biopharmaceutical Process Development – A DOE Approach
Abstract
Biologic medicines, also known as biologics, are experiencing a rapid growth in China as one of the most promising frontiers in medicine, the speed to bring a product from DNA to BLA is the key element of success for every organization in the pharmaceutical industry. To reduce the development time and establish all the critical and key process parameters, statistical design of experiments (DOE) is a valuable tool to help achieve those objectives efficiently and effectively. We have used DOE in various stages of process and method development for screening and optimization. For example, cell line engineering applied the principle for cell line screen and selection, cell culture development used DOE for culture media screening and optimization, purification process development used DOE to test characterization range and to establish optimal operating range, analytical method development established assay method with the help of DOE, and formulation development was able to screen and obtain the most suitable final product formulation buffer with a fractional factorial design in JMP. The presentation will provide an overview of these applications with case reports.
Title: Big Data Applications and Analysis in the Pharmaceutical Industry
Abstract:
Data acquisition is carried out throughout the entire product life cycle in the pharmaceutical industry, and supports GxP management.The pharmaceutical industry does not lack for data, but how can we effectively analyze and utilize such data？From application in formulation and control of QbT-assisted internal control standards to QbP control of statistical processes; from analysis and application of essential technical process fluctuations to QbD applications in product development, process R&D and analysis methods - how can we systematically analyze these processes, implement continuous iterative upgrading, and utilize JMP to examine models and apply in practice?
Background:
Peng Xiaodan is the current vice president of the operations management department of Shanghai Fosun Pharmaceutical (Group) Co., Ltd. He has a Lean Six Sigma Master Black Belt (MBB), a Master of Science in Quality Management from Hong Kong Polytechnic University, and a master's degree in International Pharmaceutical Engineering Management (IPEM) from Peking University. With extensive theoretical and practical knowledge in Statistical Process Control, hybrid modeling, experimental design, predictive modeling, statistical calculation, Peng possesses a unique perspective on fields such as quality management, data analysis and application, and operational excellence. This allows him to lead teams in implementing innovative ideas to bring huge benefits to the company. While promoting the use of JMP and other core tools for data analysis in the pharmaceutical industry, he has also nurtured many outstanding professionals.
Lecture Topic: Applications of JMP in R&D, Manufacturing and Quality Management in the Pharmaceutical Industry
Lecture abstract: Drug regulatory science is a scientific discipline that is concerned with the development of new tools, standards and methods to evaluate drug safety, efficacy, quality control and utility, and is based on data quantification. Drug safety and efficacy is based on clinical trial or observation data, and the study design and analysis methods that are relevant to this type of data fall within the realm of biostatistics. On the other hand, statistical applications related to the research, manufacturing and quality management of drug formulation and processes are classified under quality statistics. As the proportion of generic drugs used by patients increases year by year, so will the statistical requirements for pharmaceutical R&D, manufacturing and quality management domains in various national regulatory bodies. Safety problems of generic drugs are mainly attributed to quality issues during drug R&D and manufacturing, and the application of statistical methods has been proven by traditional manufacturing industries to be an effective solution. In my experience teaching and managing research, I have observed that local enterprises and drug regulatory agencies use statistical tools widely. However, they are not familiar with the prerequisites for using these tools, when to use what tools to collect what data, and the areas to focus on when using these tools. Therefore, it becomes difficult for them to understand the practical significance of key variables and parameters within mathematical formulas, and thereby realize the application of statistical methods for drug development, manufacturing and quality management.
JMP is a statistical and data analysis tool that is intuitive, effective, and widely used. Its application can greatly promote data statistics and analysis for drug R&D, manufacturing, and quality management by pharmaceutical companies in accordance with drug laws and regulations. Data analysis and usage can improve a company’s understanding of its products and processes and help achieve consensus with the regulatory agency. This presentation will focus on introducing the application of JMP in quality statistics, with practical case studies in drug R&D, manufacturing and quality management.
Jiali Luo
Vice President, Boerheringer Ingelheim China
Jiali Luo is presently the Vice President of Boerheringer Ingelheim China and the General Manager of Borheringer Ingelheim Biopharmaceuticals (China) Ltd Co. Prior to his currently position at BI, Luo took several managerial positions as Director of R&D, Director of Biopharmaceuticals, VP of Business Development, Chief Officer of Global Commercial at a major Chinese pharmaceutical company. For many years, Luo has worked in the areas of biopharmaceuticals in China and made significant contributions to the R&D, manufacture, quality system and commercial development for biopharma products. He possesses expansive experience in management and operations in the biopharmaceutical industry both in China and overseas. Luo has master’s and PhD degrees in biochemistry and protein science, as well a MBA degree.
[missing abstract for NIFDC?]
Tan De Jiang
Deputy Director of Pharmacology Laboratory, Institute of Chemical Drug Control, National Institutes for Food and Drug Control
Tan De Jiang is a Chief Technician, mentor to master’s students, and Deputy Director of Pharmacology Laboratory at the Institute of Chemical Drug Control, National Institutes for Food and Drug Control. He sits on many committees, including the Expert Committee for competency validation of the three-grade and one mechanical testing system for the China Food and Drug Administration (CFDA), the Drugs Subcommittee of the 9th National Drug Reference Standards Committee, and the Medical Laboratory and Diagnostic Product Quality Control Subcommittee for the Beijing Medical Examination Society. Tan is a CFDA Medical Device Evaluation Expert, Review Expert for the National Natural Science Foundation of China’s State Science and Technology Prize and Beijing Science and Technology Award. He is a member of the Editorial Board of the Chinese Journal of Pharmaceutical Analysis. Currently, he is a visiting professor at the Ocean University of China and a mentor to master’s students at China Pharmaceutical University and Yantai University. He previously conducted pharmacological research at Taisho Pharmaceutical Co., Ltd. in Japan and the Istituto Superiore di Sanità in Italy. His research interests are mainly focused on drug efficacy and toxicity research, evaluation and statistical applications. He has been involved in the 8th Five-Year Plan research project, 863 Program, major national projects and international collaboration projects, and he led a project for the Beijing Municipal Science and Technology Commission. Tan received the National Science and Technology Progress Award (3rd class), the State Administration of Traditional Chinese Medicine Science and Technology Progress Award (2nd class), and the Chinese Pharmaceutical Association Science and Technology Award (3rd class). He published more than 70 academic papers in local and overseas academic journals, seven of which were indexed in SCI. He also participated in compiling seven academic publications, serving as chief editor for one and deputy chief editor for another.
