In recent years, with the advent of the AI wave, many industries have been directly or indirectly affected. What applications will it have for the semiconductor industry, which is the most watched in the world? This interview invites Sonic, a data analyst who leads a semiconductor packaging and testing factory, to talk with Eric, a senior consultant at JMP Taiwan Semiconductor, to share his observations on the frontline applications of today's semiconductor industry and technology, as well as how AI and data analysis are used in semiconductor manufacturing. play a key role.
artificial intelligence Under the trend, artificial intelligence Applications in the semiconductor industry
Sonic first highlighted the key applications of AI in semiconductor manufacturing. He pointed out that semiconductor manufacturing involves multiple stages, including design, manufacturing, testing, etc., and AI can be applied in these stages. For example, in the product development and design stage, they use PCA, machine learning and other modeling techniques, based on BOM (materials) and production parameters, as the criteria for product design. For another example, when an abnormality occurs on a machine, they use database SQL combined with JMP to automatically analyze possible causes so that the factory can quickly improve. In addition, they also conduct research on deep learning and image processing.
Eric further added, pointing out that semiconductor manufacturing is not suitable for comparing the quality of products with the human eye due to the large amount of data, while AI can be applied to many aspects. He mentioned that in the field of chip manufacturing, AI can be used to determine whether different parameters or indicators have special performance in each process section, and even to find the best formula. He also emphasized the application of AI in process monitoring and error reporting, pointing out that with technological advancement, the application scope of AI tools has gradually expanded to semiconductor equipment manufacturers and packaging and testing plants.
artificial intelligence Popularity and ease of use in the semiconductor industry
Regarding the popularity and ease of use of AI in the semiconductor industry, Sonic said that it has improved significantly in recent years. He listed the following observations:
- design phase: AI can assist engineers to speed up the design process and improve efficiency, such as BOM selection & programming, etc.
- Manufacturing stage: AI can analyze manufacturing data, detect and predict manufacturing defects, and improve production efficiency and quality.
- Testing phase: The application of AI in the field of semiconductor testing is very important, such as automatically analyzing test data and improving test efficiency and accuracy.
- Tool development: The company develops AI tools and platforms for semiconductors, reducing the requirements for AI expertise and providing a more intuitive and easy-to-use interface.
He pointed out that these AI applications can help improve production efficiency, quality and innovation capabilities, and accelerate the development of the semiconductor industry.
JMP consultants emphasized that due to the rapid development of the semiconductor field, there is still a long way to go before AI can be used to control production lines. The reason is that AI tools require manual writing and adjustment, which requires a lot of time and cost, and the program is often constructed. This program is no longer sufficient and needs to be adjusted. It may be the case that AI program optimization cannot keep up with the development of semiconductors.
He emphasized that the key to solving this problem lies in whether the AI program can be easily modified and adjusted quickly. Currently, semiconductor manufacturers have used JMP as small plug-ins to help customers focus on problems faster and save development time.
Are semiconductor companies becoming intelligent? Key elements for building a smart factory
Sonic believes that before talking about the construction of smart factories, all infrastructure must be in place, such as complete sensors and Internet of Things technology to save the operating status and data of all production equipment in a database. Through statistical analysis and algorithms, data are processed and analyzed to achieve optimization and decision-making of smart manufacturing. At the same time, he emphasized the importance of personnel training and skill improvement. Enterprises also need to provide complete personnel training to cultivate talents within the enterprise.
JMP consultants added that database connection and smart chemical factory game rules, such as using programming languages to edit machine learning or AI judgment criteria, are a key to transformation. He pointed out that this was not only a financial challenge, but also a time pressure. He believes that software needs to keep up with the development of hardware, otherwise the results of early development will be affected. Therefore, before implementing a smart chemical plant, the connection of test data and analysis platforms should be improved to ensure that problems can be responded to in a timely manner and fully prepared for the implementation of smart chemical plants.
Investing in AI Previous process optimization and quality improvement
Sonic said that of course the more data that can be collected, the better, but often when there is an abnormality, existing data will be used to find the problem, such as manually analyzing the machine LOG to find the correlation and trend of the data for subsequent automatic analysis. use.
JMP consultants shared the tools provided by JMP to optimize models, including regression analysis, machine learning, decision trees, etc. He pointed out that JMP Pro provides more modeling tools, such as SVM, K-nearest Pro, random forest, etc. Through tools that optimize models, better and more accurate models can be obtained. He also mentioned the Model screening function provided by JMP Pro, which can compare all modeling platforms and find the appropriate model building platform. Finally, he shared JMP's Profiler and Design Space Profiler tools, which help find the best parameter combination and Process window.
Data analysis combines commonly used semiconductor software and EDA tool
Sonic mentioned that their company mainly promotes the use of JMP for EDA tools, and the analysis results can gain the trust and satisfaction of customers, including machine LOG analysis, anomaly analysis, quality and efficiency control and other functions. Engineers can use these tools to quickly obtain data and results, and can even flexibly start other JMP analysis tasks through data to make reports that better meet their needs.
JMP consultants mentioned the tools provided by JMP, such as Clustering for wafer problem classification and identifying potential new problems; Control Chart and JMP Live Warning Test for automatic reporting systems. These tools facilitate data analysis and real-time monitoring in semiconductor manufacturing, improving efficiency and quality.
Summarize
In this interview, we have an in-depth discussion of the application of AI and data analysis in the semiconductor industry. From design to manufacturing to testing, AI and data analysis play a significant role in each stage, promoting the continuous innovation and development of the semiconductor industry. If you want to learn more about the application of JMP in the semiconductor industry, please watch more topics: Semiconductor topic discussion: Identifying defective wafers, automatic detection and automated analysis based on JMP or Try JMP for 30 days Complete your semiconductor data analysis.
Recommended reading:
