Design and application of integrated multi-sensor electronics with self-x properties for robust intelligent systems in industrie 4.0 and IoT Domains

Abstract
The thesis presents a cost-effective approach to enhancing the autonomy and optimization
capabilities of smart sensory electronic systems (SSES) through the integration of artificial
intelligence (AI) at the lowest levels of automated test equipment (ATE). This integra-
tion aims to realize self-configuring, self-optimizing, and self-healing (”self-X”) properties
in SSES, leveraging the transformative power of machine learning to revolutionize tradi-
tional sensory systems. In the era of Industry 4.0, where the fusion of advanced digital
technologies with the physical production and operational processes defines a new indus-
trial revolution, the application of AI and machine learning in SSES represents a critical
step forward in realizing intelligent, efficient, and highly adaptable manufacturing and
production environments.