Gerard Rauwerda


Metrology in semicon & life-sciences equipment: from sensor-data to information 

Metrology, the science of measurement, plays a crucial role in advanced technology fabrication and analysis. More accurate, reliable and faster measurements are essential to drive innovation and economic growth. Such as being able to develop better instruments for high-precision fabrication of integrated circuits (ICs), photonic integrated circuits (PICs), or accurate high-precision diagnostics of biological tissues.
For acceleration of metrology systems, research is being done on systems with parallel acquisition. With this, data streams from the metrology systems grow enormously in size, which is a problem in itself. For example, for the subsequent data storage, making it available and suitable for post-processing, but also for technology exploration and opportunities to get from data to information. To gain insight about multiple aspects of an object, multiple metrology systems must be brought together for interpretation. This "workflow", for new applications, is mostly manual and time-consuming. The major challenge is to automate this process.
Within the semiconductor domain, metrology finds application in integrated photonics and semiconductor inspection machines, where three-dimensional images of the materials can be reconstructed. Artificial Intelligence (AI) can provide pattern recognition to identify relevant material features or to detect defects in the materials. Also, insight into the structures of materials and tissues at the nano level is increasingly being used for research and inspection of materials and molecular structures in e.g. the life sciences domain.
Control at the nano-precision level combined with extreme, high-speed data acquisition from the relevant sensors is a central challenge here, as well as developing the required workflows within laboratories and production environments.
To achieve our development goals, we see 3 primary knowledge themes for advancing next-generation metrology equipment:
1) Machine Control: machine control concepts of metrology machines, and then in particular whether it is possible to use a generic control framework/platform to address different use cases and market demands.
2) Data Handling: data-to-information management, such as a data management platform to efficiently store (multi-modal) data and effectively convert it to information.
3) Information Extraction & Learning: extract targeted information from data with data analysis techniques, such as hybrid AI, AI and computational learning.
We will present our contributions to the development goals using examples from a multi-beam electron microscope development as well as an atomic force microscope development.

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