ST leverages Optimal+ tools for automotive, IoT ICs

Nov. 16, 2016

Fort Worth, TX. At ITC Tuesday, Roberto Lissoni, ST corporate quality director, delivered a presentation titled “Manufacturing Test Challenges for IoT and Automotive Market Segments.”  The presentation was another in a series of sessions hosted by Optimal+ to highlight its customers’ experiences using Optimal+ software. ST has worked with Optimal+ for five years, Lissoni said.

ST operates 11 manufacturing sites worldwide, Lissoni said. The goal is to produce microelectronics devices that make a positive contribution to people’s lives. The company focuses on automotive, discrete-power, analog, industrial, power-conversion, MEMS, ASIC, and general-purpose and secure MCU and EEPROM applications. ST owns the core process, extending from technology and product development through assembly and test.

Automotive and IoT applications (for the smart home, smart city, and smart industry) were the focus of his Tuesday presentation. Key issues with IoT include time to quality, manufacturing efficiency, and test flexibility to handle changes in demand. Automotive applications require defect levels in PPB, outlier detection, and zero quality excursions through a solid excursion eradication program.

Both applications require an overall efficient and effective flexible test infrastructure with real time performance management, he said.

ST began working with Optimal+ in 2011 to deploy testing as a strategic asset to increase company performance. The goal was to break silos, focus resources and competencies, and converge customer needs with proper test solutions. Results to date have included increased efficiency, the ability to monitor real-time status of testers and tester performance, weak-signal early detection, productivity increases, and the ability to make OEE measurements covering all test platforms and operations.

He said that as an example, Optimal+ tools enabled early detection of anomalies and marginalities to reduce tester stoppages and the ability to anticipate test-cell controller alarms, bringing estimated gains of 1% in test-cell utilization. He also cited benefits in online and offline retest reduction through optimized intelligent retest.

He concluded by saying big data is the new wave of structural change in the semiconductor industry, resulting in cultural changes giving engineers access to analytics and predictive information. He said we can use data science algorithms to predict failures before they happen in a preventive, not reactive, way. He described ST as a “brain-on data-centric company” merging expertise in semiconductors, computer science, and math and statistics.

Sponsored Recommendations


To join the conversation, and become an exclusive member of Electronic Design, create an account today!