Optimizing Semiconductor Manufacturing Yield with IoT
Technology Category
- Analytics & Modeling - Machine Learning
- Functional Applications - Manufacturing Execution Systems (MES)
Applicable Industries
- Equipment & Machinery
- Semiconductors
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Additive Manufacturing
- Manufacturing Process Simulation
Services
- Data Science Services
- Testing & Certification
The Customer
Not disclosed
About The Customer
The customer is a large U.S.-based manufacturer of high-performance semiconductors. The company has a broad portfolio of cutting-edge products, including radio frequency filters, amplifiers, modulators, attenuators, and more. The company has a significant global presence with 35 facilities worldwide and employs 6,000 people. The company's products are widely adopted in leading mobile devices and IoT ecosystems. The company generates $3.6 billion in revenue annually.
The Challenge
A large U.S.-based manufacturer of high-performance semiconductors was facing a significant challenge in optimizing the manufacturing process of its wireless products. The company, which designs and delivers a broad set of cutting-edge products including radio frequency filters, amplifiers, modulators, attenuators, and more, was experiencing lower than expected overall yield in some of its most complex products. This was affecting the company's productivity and profitability, and there was a need for a solution that could predict low-yield wafers early in the process and identify process improvements to increase overall yield.
The Solution
The manufacturer implemented C3 AI Process Optimization to address the challenge. This solution enabled the company to identify bad wafers, quantify time and costs saved, and tune design and manufacturing processes to optimize yields. The implementation of the project took 10 weeks from kickoff to pre-production application. During this period, a 3 terabyte unified data image of 830,000 files was created, along with more than 1,500 relevant features. The company also built 20 machine learning algorithms to predict die quality and 12 machine learning algorithms to predict the bottom 10% and 20% of low-yield wafers. The C3 AI Process Optimization user interface was configured to deliver insights on manufacturing optimization based on analysis.
Operational Impact
Quantitative Benefit
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