Semiconductor Manufacturing Equipment

Our lead customer in Industrial Systems is the world leader in lithography based advanced semiconductor manufacturing equipment using high-tech hardware and highly sophisticated software. They have been using Verum’s Dezyne predecessor, the ASD:Suite, for more than 7 years and have contributed to a range of case studies and papers related to the use of our tools.

Case Studies and Papers

Modelling in an Industrial Setting

In this presentation, Professor Jan Friso Groote of the Eindhoven University of Technology describes the use of Verum’s ASD:Suite toolset (Dezyne’s predecessor) in an industrial setting. For semiconductor manufacturing equipment he states that “[Development] Efficiency [was] up by a factor 2-3. Learn in time down by a factor 2-3. Quality: the number of problems in the field did go down dramatically”. He concludes his presentation with “Model based design and verification of behaviour leads to a 10-fold increase in quality and a 3-fold increase in development speed.”

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Assessing the Quality of Tabular State Machines through Metrics

Software metrics are widely used to measure the quality of software and to give an early indication of the efficiency of the development process in industry. There are many well-established frameworks for measuring the quality of source code through metrics, but limited attention has been paid to the quality of software models. In this article, we evaluate the quality of state machine models specified using the Analytical Software Design (ASD) tooling. We discuss how we applied a number of metrics to ASD models in an industrial setting and report about results and lessons learned while collecting these metrics. Furthermore, we recommend some quality limits for each metric and validate them on models developed in a number of industrial projects.

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Interface protocol inference to aid understanding legacy software components

More and more high tech companies are struggling with the maintenance of legacy software. Legacy software is vital to many organizations, so even if its behavior is not completely understood it cannot be thrown away. To re-factor or re-engineer the legacy software components, the external behavior needs to be preserved after replacement so that the replaced components possess the same behavior in the system environment as the original components. Therefore, it is necessary to first completely understand the behavior of components over the interfaces, i.e., the interface protocols, and preserve this behavior during the software modification activities.

For this purpose, we present an approach to infer the interface protocols of software components, from the behavioral models of those components learned with a blackbox technique, called active automata learning. We then perform a formal comparison between learned models and reference models ensuring the behavioral relations are preserved. This provides a validation for the learned results, thus developing confidence in applying the active learning technique to reverse engineer the legacy software components in the future.

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Applications

Wafer Handling

industrial systems

Metrology

Beam Control

Lot Control

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