Keysight Unveils Machine Learning Toolkit

Keysight Technologies, Inc. has released the new Machine Learning Toolkit in the latest Keysight Device Modeling Software Suite. This new solution reduces model development and extraction time from weeks to hours, according to Keysight Technologies.

Keysight’s new Machine Learning Toolkit, featuring an ML optimizer, auto-extraction flows, and utilities within Device Modeling MBP 2026 introduces a framework that combines advanced neural network architectures with ML-based optimization. Using this toolkit, auto-extraction can reduce the parameter extraction steps from over 200 to fewer than 10, according to Keysight.

Key Features and Benefits

• Accelerated Parameter Extraction: Reduces hundreds of manual steps to 5 to 6 automated steps, enabling global optimization of 80+ parameters in a single run, capturing secondary effects, temperature variations, and dynamic behaviors. 

• Automated Workflow: Integrates with Keysight’s Device Modeling platform, supporting Python-based customization and automated modeling flow.

• Scalable Across Technologies: Workflows adapt to FinFET, GAA, GaN, SiC, and bipolar devices.

• Improved DTCO Efficiency: Enables faster feedback loops between device and circuit design, reducing PDK development cycles from weeks to days. 

“AI/ML is fundamentally transforming the traditional workflows and methodologies of compact modeling," says Nilesh Kamdar, general manager of Keysight EDA. "With the new Machine Learning Toolkit, we empower our customers to deliver more predictive, higher-quality models in significantly less time—accelerating PDK development and helping them keep pace with rapidly evolving semiconductor technologies.”

For more details, visit Keysight Device Modeling Solutions. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.