Room: Florentine III
Organizer: Peter Sarson, ams AG
Moderator: Peter Sarson, ams AG

• Data Mining for Quality Related
  Sada Tomoyasu (Thine Semiconductor, JP)

  Abstract: In order to comply with ISO/TS16949:2009, it requires taking preventive action and continuous improvement for automotive quality such as conducting investigation to reduce dispersion. THine Electronics, Inc., developing long distance signal interface of mixed-signal LSI as featured technology which is suitable for the automotive application, have been analyzing accumulated data as prevention. Easy prevention will be realized by screening test and tightened criteria, but essential prevention should be achieved by improvement of the process with trusting relationship between foundries and fabless. For the relationship, it is important for fabless to provide valuable feedback of test results. This proposal describes that THine have been trying to develop our own analysis system which contributes improvement of the process and brings us to achieve customer satisfaction. The fabless have selected foundry, tester type and so on to suit with products so far. From now, it can be assumed that there is plenty or variety of data will be included in selection conditions because it has a great value that the foundry and/or tester can output various data.

• MEMS Sensor Test
  Matthew Lewis (Bosch, GE)

  Abstract: MEMS sensors are used extensively in a wide range of consumer and automotive products. With respect to automotive sensors, modern cars can contain a hundred separate sensor that aid functions from Safety and Comfort, to Performance and the Environment. With the tightening of safety and pollution regulations worldwide, and as new features such as autonomous driving are developed and realized, the proliferation of sensors in cars will continue into the foreseeable future. Bosch, as a world leader in high quality Automotive and Consumer MEMS products, is heavily involved in the development, production, and testing of a wide range of MEMS sensors. This talk will highlight Bosch’s contribution to the automotive MEMS market, while providing a basic introduction into MEMS based sensors. It will then give an overview of how these sensors are produced and tested at our world class facilities in Reutlingen, Germany.

• Performances analysis of test and burn-in application for automotive products
  Federico Venini(Polito, IT), Ernesto Sanchez (Polito, IT), Paolo Bernardi (Polito, IT), Giampaolo Giacopelli (STMicroelectronics, IT), Giorgio Pollaccia (STMicroelectronics, IT), Davide Appello (STMicroelectronics, IT)

  Abstract: Burn-in is a stress and test practice widely utilized to screen devices usually targeted to quality sensitive application segments. From the operational point of view, burn-in represents a high cost operation. Its execution implies the utilization of assets like climatic chambers and electronic drivers. During burn-in devices are loaded on burn-in boards (BIBs) whose lifetime is limited by the harsh conditions they are exposed. BIBs are usually a major contributor to the overall cost of test. In this context, we’ve considered a typical test and burn-in application for a digital product targeted to the automotive market, and we analyzed it in various perspectives. One of the goals is to embed in the burn-in step also the largest possible quantity of tests usually applied at final test, to take advantage of the much higher parallelism. We will report about the debug of yield issues, which are dependent on the overkill induced by test instabilities largely caused the ageing of materials used in BIBs. The achievement of sufficiently high yield and test accuracy enables the monitoring of the early failure rate figures directly in the line. This also enables 100% production monitoring, and progressive tuning of duration. The paper will show the effects of used test methods respect yield. We will also report how different test methods can be used to control electrically the thermal activation of the IC, on top of the environmental thermal conditioning.