Burst pressure is an important parameter for pressure sensors, as it determines the system safety of measuring devices and entire plants in the food, pharmaceutical, energy, and chemical industries. According to the current state of the art, burst pressure can only be determined destructively on individual test samples. In addition to sensor design, the quality of silicon direct bonding is the decisive parameter for the burst pressure level.
The new “ZEBA” research project is investigating image-property relationships, whereby certain non-destructive imaging tests allow qualitative conclusions to be drawn about burst behavior, but no physical-analytical simulation model for determining the probable burst pressure exists to date, meaning that quantitative statements are not possible. The aim of the investigations is to develop an AI-supported process flow that can be integrated into the manufacturing process and provides non-destructive statements about the expected burst pressure of the individual chip.
The research and development work described was funded by the German Federal Ministry for Economic Affairs and Energy (BMWE) as part of the research project “Non-destructive image evaluation using AI-supported imaging” (ZEBA).
Funding code: 49VF250012
