Hierarchical modeling of automotive sensor front-ends for structural diagnosis of aging faults

The semiconductor industry for automotive applications is growing rapidly. This is because advanced electronics is now being developed to monitor and control many vital functions previously handled purely mechanical. In addition hybrid and pure electrical cars are emerging. Parts of these electronic systems have strict safety-critical requirements, while operating in a harsh environment. Although functional diagnosis is currently the norm, many occurring faults during lifetime, e.g. due to aging, cannot be diagnosed. This poses serious threats during operation as correction for dependability is not possible in this case. This suggests the introduction of structural diagnosis techniques. Major problem is that a number of different hierarchies have to be considered and reuse of reliability data at different hierarchies should be possible. This paper investigates a new approach for the development of dependable analogue/mixed-signal car front-ends, by interfacing aging models between different hierarchies enabling structural diagnosis, and explicitly using simultaneously design and simulation data as well as built-in observation measurements at all hierarchies.