PathWave device modeling (IC-CAP) Software

Keysight EDA 2025 for Device Modeling

Elevate your design intelligence with Python automation and AI / ML. IC-CAP 2025 integrates AI technology to significantly enhance the automation and accuracy of device modeling extraction, reducing flow complexity and improving consistency. MBP 2025 includes a new Python automation framework to assist engineers in creating fully automated flow runs in a few minutes instead of hours for the typical manual corner tweaking process.

Highlights

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Device Modeling IC-CAP 2025

Device Modeling IC-CAP 2025 highlights:

  • New Keysight ML Optimizer - enables AI for device modeling
  • New Model Generator QA - facilitate Model QA during the extraction
  • New Model Generator Utility tools - easily create Suites and Model Templates
  • Model Generator examples BSIM-BULK, BSIM-CMG and BSIM4
  • Enhanced CMC GaN RF packages – now includes ASM-HEMT ANN Hybrid model
  • Python 3.12 upgrade - new packages available
  • Support for new OS: RHEL8, RHEL9, and SUSE 15

The new ML Optimizer is part of the existing W7010E IC-CAP Analysis, and the W7008E Model Generator Advanced Tools add-on now includes the Model Generator QA and Model Re-centering. 

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Machine-Learning-Based-Automatic-Parameter-Extraction

SPICE Model Verification in IC-CAP Model Generator

Self-heating and trapping enhancements in GaN HEMT models

Model Generator Re-Centering Tool in IC-CAP

IC-CAP 2025 integrates AI technology to enhance the automation and accuracy of device modeling extraction significantly. The new Keysight Machine Learning (ML) Optimizer successfully extracts numerous parameters from extensive data, reducing flow complexity and improving consistency. Additionally, new features such as the Model Generator QA and the Utilities Toolkit facilitate the management and organization of modeling extraction projects. The IC-CAP Model Generator framework streamlines data import, organization, and filtering and adds parameter extraction steps with tuners/optimizers or Python scripts.

IC-CAP Model Recentering

Model Recentering with the new AI/ML Optimizer.

IC-CAP Demo Videos

A new machine learning algorithm in IC-CAP reduces device modeling time from days to hours. It enables fully automatic parameter extraction, significantly improving modeling efficiency by putting everything  in a single step. The fully automatic modeling flow is easily repeatable and reusable, providing value in optimizing device modeling processes.

Discover the benefits of hybrid artificial neural network (ANN) models in improving the efficiency and accuracy of device modeling for technologies without reliable models. By integrating measurement-based and physics-based approaches, these models effectively address the complexities of device behavior. The enhanced performance is demonstrated through examples of PowerMOS and GaN HEMTs, utilizing a user-friendly ANN modeling toolkit that simplifies the modeling process.

Discover how the new model generator in IC-CAP revolutionizes device modeling by improving speed, usability, and customization. Learn how to streamline data management, create custom extraction flows, and generate FOM plots, all while reducing programming effort and enhancing productivity by up to 30%.

Device Modeling MBP 2025

MBP 2025 includes the following new features and enhancements:

  • New Python support enables users to build a more efficient and complete automation modeling flow 
  • New Python Editor
  • New Python Examples 
  • Support for corner check in QA Express
  • New Parallel simulation for MOSFET with MBP internal engine

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Qualify CMOS Models with QA Express in MBP

Learn how Keysight's new MBP Python capabilities can help your engineers create fully automated flow runs in a few minutes instead of hours for the typical manual corner tweaking process.

MBP Python Script Editor

MBP Python Script Editor

PathWave Model QA Report Option

Model QA report option.

Device Modeling MQA 2025

MQA 2025 includes the following new features and major enhancements:

  • Enhance the SaveTable() function
  • Support new options of Monte-Carlo simulation for Spectre
  • Python 3.12 upgrade

Advanced Low-Frequency Noise Analyzer (A-LFNA) 2025

A-LFNA 2025 includes the following new features:

  • Improved PXIe SMU (M9601A) driver for faster DC measurements.
  • Support for 'AutoRoll-off Cancellation' during FNoise measurements
  • New Routine for auto RTN detection
  • New Input Range mode for M3102A/M3100A
  • Support for CSV file saving for Auto RTN Judgement with  Ton/Toff RTN Analysis results
  • Reduced total RTNoise measurement time consumption
  • Support for 'delta-Threshold' and its calculation for Ton/Toff RTN Analysis

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How to Determine the Low-Frequency Noise Source in Cryogenic Operation

Launch A-LFNA Software from Another Application

Introducing A-LFNA software's new Auto Roll-off Cancellation. The wide band low-frequency noise is an important characteristic for modeling and process engineers. However, the bandwidth is limited by parasitic resistance and capacitance. Auto Roll-off Cancellation can expand the bandwidth to the hardware limitation.

A-LFNA Roll-off Cancellation

A-LFNA Roll-off Cancellation

Device Modeling and Characterization Software and Hardware

Featured Resources

Application Notes 2024.09.27

Machine Learning-Based Automatic Parameter Extraction

Machine Learning-Based Automatic Parameter Extraction

The new ML optimizer in IC-CAP can enhance device modeling efficiency and boost productivity, reducing the time required from days to hours. The robust device modeling capability is demonstrated through the use case of model re-centering and BSIM4 modeling. With the Model Generator, users can seamlessly import target data and create scaling plots for tuning or optimization. The new ML optimizer enables fully automatic parameter extraction by simply selecting the desired targets for adjustment and choosing the associated model parameters in a single step, as opposed to a series of optimizations with the old optimizers. This results in a substantial enhancement of modeling efficiency within a short timeframe.

2024.09.27

Application Notes 2024.09.25

SPICE Model Verification in IC-CAP Model Generator

SPICE Model Verification in IC-CAP Model Generator

IC-CAP Model Generator QA provides a new tool for quick simulation QA on Model Generator. It allows you to extend simulation conditions, compare with two simulators, and automatically generate reports. This application note introduces the usefulness of those features.

2024.09.25

Brochures 2024.04.26

Keysight EDA Solutions for Semiconductor Foundries

Keysight EDA Solutions for Semiconductor Foundries

In this brochure, we explore the critical factors foundries must consider at each stage of the device modeling and PDK development flow. By showcasing Keysight EDA's comprehensive solutions, we demonstrate how it becomes a key enabler in driving the evolution of ICs beyond Moore’s Law.

2024.04.26

Case Studies 2024.04.09

Determined the Low-Frequency Noise Source in Cryogenic Operation

Determined the Low-Frequency Noise Source in Cryogenic Operation

Japan's National Institute of Advanced Industrial Science and Technology (AIST) successfully the measurements of random telegraph noise (RTN) in short-channel MOSFETs at cryogenic temperatures using the Keysight E4727B Advanced Low-Frequency Noise Analyzer is expanding the understanding of noise sources in quantum computing, paving the way for faster machines moving forward.

2024.04.09

Application Notes 2024.01.31

Model Generator Re-Centering Tool in IC-CAP

Model Generator Re-Centering Tool in IC-CAP

This is an application note for the new IC-CAP feature: Recentering Tool

2024.01.31

Application Notes 2023.09.27

Qualify CMOS Models with QA Express in Model Builder (MBP)

Qualify CMOS Models with QA Express in Model Builder (MBP)

This application note will discuss the functionalities of QA Express in MBP and best practices to pinpoint and address QA issues during the parameter extraction.

2023.09.27

Application Notes 2023.09.08

Trapping Extraction of GaN HEMTs

Trapping Extraction of GaN HEMTs

Trapping effects modeling in GaN devices is a complex and interdisciplinary field. Accurate modeling of trapping effects is essential for optimizing semiconductor device performance, reliability, and functionality. The parameter extraction of ASM-HEMT and MVSG_CMC in PathWave Device Modeling (IC-CAP) includes trapping modeling.

2023.09.08

Application Notes 2023.08.11

Keysight W7801B New De-Facto Standard Software for B1530A (WGFMU)

Keysight W7801B New De-Facto Standard Software for B1530A (WGFMU)

The W7801B software is a new de-facto standard software to control Keysight B1530A (WGFMU). The software covers both RTN and 1/f noise measurement from a single platform and continuous measurement. It is running on PathWave WaferPro and allows you to control the Prober Station and Switch Matrix and it can provide the very efficient low-frequency noise measurement.

2023.08.11

Application Notes 2023.08.31

How to Extract the ASM-HEMT Model for GaN RF Devices Including Thermal Effects

How to Extract the ASM-HEMT Model for GaN RF Devices Including Thermal Effects

A comprehensive extraction procedure for the industry-standard ASM-HEMT model for GaN devices, including self-heating. This strategy is included in PathWave Device Modeling (IC-CAP) as an example.

2023.08.31

Application Notes 2022.07.25

Tips: How to Optimally Measure LF Noise

Tips: How to Optimally Measure LF Noise

The E4727B Advanced Low-Frequency Noise Analyzer enables fast, accurate and repeatable low-frequency noise (LFN) measurements on numerous device types. Now, thanks to tight integration with Keysight’s WaferPro Express software, device modeling and characterization engineers can now add noise measurements to a larger suite that includes high-speed DC, capacitance and RF S-parameter measurements, all the while automating wafer prober control.

2022.07.25

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