Why Use Integrated Circuit Characterization and Analysis Program (IC-CAP)
The industry standard for advanced device modeling.
Accurate SPICE models begin with trusted characterization and extraction technology. For decades, foundries and IDMs worldwide have relied on IC-CAP to automate measurements, extract model parameters, and develop accurate compact models for digital, analog, RF, and power applications.
Combining advanced automation, open customization, AI-enhanced extraction technology, and broad support for measurement instruments and simulators, IC-CAP helps engineering teams reduce development cycles, improve model accuracy, and accelerate technology enablement from characterization to PDK delivery.
Key Capabilities
Enhance automation and precision in device modeling workflows.
Pre-built flows for BSIM3, BSIM4, PSP, HiSIM, and other industry-standard CMOS models.
Direct links to major commercial simulators ensure consistency with circuit design environments.
Built-in Python expanded package support for data processing, automation, instrument integration, and visualization.
Resources to Get You Started
Keysight Device Modeling software bundles provide device modeling engineers with a choice of valuepacked, pre-configured combinations of software built for specific device modeling design challenges. These combinations enable you to address your measurement and modeling needs efficiently.
Demos
Automatic Re-centering Enabled by Machine Learning
Demos
Device Model Generation in Less Time
Demos
Device Modeling with Hybrid Physical Model and ANN
Technical Overviews
Device Modeling IC-CAP
eBooks
The Essential Guide to GaN Semiconductor Device Modeling
Application Notes
How to Extract the ASM-HEMT Model for GaN RF Devices Including Thermal Effects
Application Notes
Trapping Extraction of GaN HEMTs
Case Studies
IIT Kanpur Develops ASM-HEMT Model for GaN Transistors Using Parametric and Device Characterization
Explore IC-CAP Bundles
ADS software bundles provide designers with pre-configured combinations targeted to a specific design workflow.
PathWave IC-CAP Device Modeling Platform
All the tools and examples needed to characterize and model semiconductor devices. Includes the software environment, all measurement drivers, optimization, Python/PEL programming and all simulation links.
All the tools and examples needed to characterize and model semiconductor devices. Includes the software environment, all measurement drivers, optimization, Python/PEL programming and all simulation links.
PathWave IC-CAP Wafer Professional Measurement
WaferPro for automated wafer-level measurement. All measurement drivers are included. Python/PEL programming enables custom measurement algorithms and drivers.
WaferPro for automated wafer-level measurement. All measurement drivers are included. Python/PEL programming enables custom measurement algorithms and drivers.
PathWave IC-CAP CMC GaN RF Modeling
Measure and accurately model any GaN RF device with the physics-based industry-standard ASM-HEMT and the MVSG models, or use the established Angelov-GaN model.
Measure and accurately model any GaN RF device with the physics-based industry-standard ASM-HEMT and the MVSG models, or use the established Angelov-GaN model.
Related MBP Bundle
PathWave MBP/IC-CAP Modeling
Includes Model Builder (MBP) core environment and Device Modeling (IC-CAP) core environment, simulation and analysis module.
Includes Model Builder (MBP) core environment and Device Modeling (IC-CAP) core environment, simulation and analysis module.
Related MQA Bundle
PathWave MQA/IC-CAP Modeling
Includes Model QA (MQA) core environment and Device Modeling (IC-CAP) core environment, simulation and analysis module.
Includes Model QA (MQA) core environment and Device Modeling (IC-CAP) core environment, simulation and analysis module.
Have Questions? We've Got Answers
Semiconductor device modeling involves creating precise mathematical models that simulate the electrical behavior of semiconductor devices and circuits in response to various stimuli. A device model includes a set of equations with specific parameters or coefficients. Determining parameter values is a meticulous process that involves either direct extraction or optimization techniques to ensure the model equations closely fit the measured data.
Semiconductor device modeling forms the foundation of process design kits (PDKs), offering mathematical models that represent the behavior of the semiconductor devices, such as transistors, diodes, and capacitors, within the integrated circuits (ICs). As semiconductor device sizes shrink to the nanometer scale, device behavior becomes increasingly complicated as new physical phenomena occur. Accurate models enable design engineers to simulate and predict the behavior of devices before moving to costly physical prototyping.
Device modeling falls into the following categories:
- Physics-based models: Engineers recognize physics-based device models for their accuracy, as they rely on fundamental physical laws. One example is the 2D solution of Poisson's equation, which describes the electrical potential distribution in semiconductor devices.
- Empirical models: Derived by fitting equations to experimental data, these models prioritize practical predictability over theoretical foundations. For instance, the Curtice empirical model describes the I-V characteristics of metal-semiconductor field-effect transistors (MESFETs) and high-electron-mobility transistors (HEMTs).
- Semi-empirical models: These models combine physical principles with empirical data. For instance, Gummel-Poon is a semi-empirical model for bipolar junction transistors (BJTs).
- Table-based models: Table-based models, such as the HP Root FET model, interpolate measured data to simulate device behavior under different conditions.
- Artificial neural network (ANN) models: These models use artificial intelligence algorithms to learn from data and predict semiconductor device behavior. For instance, the DynaFET model applies neural networks to simulate the dynamic behavior and nonlinearities of field-effect transistors (FETs).
Modeling today’s industry-standard transistors is a complex and time-consuming process involving massive amounts of data. Extracting and verifying the quality of a Simulation Program with Integrated Circuit Emphasis (SPICE) model library can take weeks to months. As process technology advances, the model library needs continuous updates. Furthermore, the adoption of wide bandgap semiconductors relies on robust models for gallium nitride (GaN) devices to simulate highly nonlinear device behavior, including thermal and dynamic memory effects.
Semiconductor device modeling involves four basic steps to ensure accuracy and efficiency.
- Data measurement and analysis: This initial step measures the device’s physical and electrical properties, requiring extensive data collection. The procedure to select a typical die for measurements varies depending on the process development stage.
- Parameter extraction: This step involves extracting critical parameters such as DC, CV, S-parameters, and noise, which define the device's behavior under various conditions. The challenge for the modeling engineer is to implement a repeatable and robust procedure to extract the parameters of the model such that the mathematical model representation will match the measured data.
- Modeling: This step creates a mathematical model that accurately represents the device's behavior with the extracted parameters.
- Model validation: Validating the model ensures its reliability before it becomes available to users. Besides verifying the model's accuracy against measured data, modeling engineers must test its robustness by simulating it across multiple biases, geometry, and frequency conditions.