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Device Modeling IC-CAP 2025 Product Release
Highlights
IC-CAP 2025 includes the following:
- New AI-enabled ML Optimizer enables simultaneous extraction of model parameters
- New Model Generator QA, Bias Point Operating Manager, and Configuration Tools
- New Model Generator examples for BSIM-BULK, BSIM-CMG, and BSIM4
- Enhanced CMC GaN RF extraction packages - now including ASM-HEMT ANN Hybrid model
- Python 3.12.2 upgrade - new and updated packages available
- New OS support: RHEL8, RHEL9, and SUSE 15
- New instrument drivers for Keysight PZ2100A Precision SMU, NA520xA PNA-X Pro, E5080B ENA, E5081A ENA-X, and Streamline Series VNA
IC-CAP 2025 is available now!
The AI-enabled ML Optimizer reduces model extraction time from days to hours.
Enabling AI for Device Modeling
Device Modeling (IC-CAP) 2025 features a new Machine Learning (ML) Optimizer designed to streamline and automate the parameter extraction process for complex compact device models. It addresses the limitations of traditional gradient-based optimizers by leveraging derivative-free machine learning techniques to explore the complex, high-dimensional parameter spaces efficiently. This results in faster convergence, improved solution quality, and increased automation, reducing extraction time from days to hours. The new ML Optimizer is part of the existing W7010E IC-CAP Analysis product, and it can be applied to the extraction of any compact model. Several example projects are provided to get you started and help you integrate the new Optimizer into your extraction flows.
IC-CAP 2025 also features significant improvements in the Model Generator (MG). We have added new tools to improve productivity, such as the Model Generator QA, and increased the speed of certain operations, such as loading data and creating scaling plots. MG now includes complete extraction example projects to provide a good starting point for learning the tool.
IC-CAP 2025 now integrates and ships Python 3.12.2, including new and updated packages. Existing scripts based on Python 3.8 may require minor changes to avoid warnings/errors. For guidance, please refer to the official Python documentation at https://www.python.org/
Model Generator Tools and Updates
The Model Generator (MG) framework simplifies and manages modeling extraction projects. With just one click, you can import and organize various data types, including scaling plots, preview and filter them before modeling, and quickly add parameter extraction steps with tuners/optimizers or Python scripts. New in MG 2025 is the addition of several productivity tools:
- Model Generator QA (MGQA) - enables simulation outside the measurement ranges to assess model robustness. It also compares simulated results from two simulators or two model cards. Additionally, MGQA generates reports summarizing the results.
- MG Suite Configurator - a new wizard to help users set up custom Suite and Model Templates when they start working with the Model Generator.
- MG Operating Condition Manager allows the user to control key project variables, such as bias and frequency operating point conditions, to efficiently update scaling plots.
In addition, MG 2025 features new usability and performance improvements. Defining and managing Scaling Plots, importing netlists, and managing optimizer ranges is now easier. Loading measured data and updating Scaling Plots is now 5 to 10x faster.
The new W7008E Model Generator Advance Tools add-on includes Model Generator QA and the existing Recentering and Targeting modeling. The MG works with any device model and ADS or HSPICE simulators; however, to maximize performance, Keysight's PathWave Advanced Design System (ADS) is recommended.
IC-CAP Model Generator: enhance your productivity by 30%.
Enhancing model accuracy with IC-CAP Hybrid-ANN modeling.
Hybrid ANN Modeling for GaN Devices
IC-CAP 2025 introduces the concept of Hybrid-ANN. The classic ANN approach creates a neural network-based model pre-trained on measured data. While this model can be very accurate, it is a black-box model and offers no specific insight on the device. On the contrary, Hybrid physical-ANN modeling offers a powerful new approach to device modeling, combining the advantages of physics-based models and data-driven ANNs. The original compact model is maintained and augmented with ANN-based elements carefully targeting second-order effects. The combined model offers superior accuracy while maintaining the structure and parameters of the original compact model.
Keysight's IC-CAP and the new ANN Modeling Toolkit provide a user-friendly environment for implementing this methodology. This approach enables faster model development and improved accuracy for complex device behaviors and novel technologies. It addresses modeling issues with minimal programming, enhances modeling accuracy quickly, and builds ANN frameworks quickly.
IC-CAP 2025 includes an example of a hybrid ANN model extraction applied to the ASM-HEMT industry-standard model for GaN devices. The W7009E IC-CAP ANN Modeling Toolkit is necessary to extract Hybrid-ANN models.
For more information on this release, refer to the IC-CAP 2025: Introducing AI for Device Modeling presentation.
Get Started
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Looking for another version? View other Device Modeling IC-CAP Product Versions.
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