KEYWORDS: Data modeling, Education and training, Performance modeling, Modeling, Mathematical optimization, Quantum experiments, Data processing, Process engineering, Process modeling, Quantum processes
A digital twin is a numerical copy of an asset or a process, used to predict its physical behavior over time. Usually, a digital twin is based on physical models, constructed by simulating its different parts. It is then used to monitor and act on systems, based on digital state information, which is computed from real sensors data that feed the digital twin. Among the usages, we can cite predictive maintenance, planification, root cause analysis among others. We propose to adapt the technology to monitor and model complex processes by data driven, it can also be used in complement of physical simulation. Our proposal is a framework to create Artificial Intelligence (AI) models based on experimental data, then simulate new recipes and optimize the process, including constraints defined by the Process Engineer. AI models can be enriched with physical models; when available, they are used to create additional training data and to compare AI models with simulation. AI models require clean data, this procedure is tedious and time consuming. Depending on the process, it can be simplified by proposing automatic processes to clean and arrange data so that it can be used directly for training. The use of AI in comparison to classical physical models allows users to identify bias in their selection of parameters. It is used as a proxy for accurate optimization of the process under constraints. It can also serve to explore more efficiently the parameters space, by avoiding experiments that would lead to low performances. Finally, several tools are proposed to improve the understanding of the complete process and visualize the relationships between parameters and characteristics of the product. We propose an experimental setup using physical simulations of semiconductor materials to demonstrate the use of our digital twin pipeline.
KEYWORDS: Edge detection, Metrology, Scanning electron microscopy, Data modeling, Transmission electron microscopy, Deep learning, Semiconductors, Critical dimension metrology
Edge detection is the core of most metrology tools to identify boundaries between materials. With the shrinking in size of all devices for higher performances, this task becomes more and more challenging. In addition, the use of new composition of materials increases the challenge by reducing the contrast between different materials. The general quality of the images is critical to recover the right edges, in particular for High Resolution Electronic Microscopy, which remains the reference for high quality metrology during R&D phases. In these images, the low contrast of the edges and changes in the texture of the materials creating no sharped borders are impacting the performances of classical edge extraction methods. In order to improve capacities of algorithms, we propose a pipeline to generate edge maps. These edge maps can be further used in more classical algorithms to extract better measurements. Two versions of the pipeline can be used, a first one trained on generic images from the semiconductor industry and a second one that can be tuned on specific use cases. The first one provides a ready-to-use solution and the second one is able to provide more accurate measurements at the cost of annotating images. To illustrate these results, we demonstrate the use of these features with various algorithms on use cases from the semiconductor industry such as gates and wires. We propose comparison between the two pipelines both in term of accuracy and time to recipe.
The research and development steps in the semiconductor industry require tools that are able to handle features with large variation across the images, but also tools that can reproduce the definition of an edge taught by an expert. This definition should be easily modified to mimic the expert decisions in order to reduce the time spent by process engineers during research and development phases. We developed a patterned edge model allowing to detect the profile of patterned objects in microscopic images. A complementary tool is proposed to customize the definition between two materials according to the expert targets. The obtained profiles serve as a basis to perform robust metrology and ensure quality control of the manufactured semiconductor components.
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