Samsung Foundry Adopts Keysight Radio Frequency Device Model Generator

Keysight Technologies, Inc. (NYSE: KEYS) announced that Samsung Foundry has adopted the new IC-CAP Model Generator (MG) to accelerate circuit library creation for its advanced RF process technologies. Python-based automation lets engineers organize large volumes of measured data, cutting the time needed to extract transistor models.

Modeling RF FinFETs is complex and time-consuming, involving massive amounts of data. The MG handles data management and multi-device simulation, freeing engineers to focus on custom extraction flows.

Modeling today's industry-standard RF fin field-effect transistors (FinFET) is a complex, time-consuming process involving massive amounts of data. RF modeling engineers must extract hundreds of parameters to accurately represent device behavior across different sizes and temperatures.

The workflow generates large volumes of measured data, including S-parameters and low- and high-frequency noise measurements, that must be carefully organized across geometries and temperatures before circuit models can be extracted and verified.

For Samsung Foundry, the challenge was to accelerate the creation of circuit libraries for its advanced RF semiconductor process technologies, while giving modeling engineers the flexibility to build and automate their own custom extraction flows.

Samsung Foundry adopted the new Keysight IC-CAP Model Generator (MG) to accelerate the creation of circuit libraries, a key component of process design kits (PDKs), for its advanced RF semiconductor process technologies.

Built on Python 3, the MG software is part of Keysight's flagship modeling platform, PathWave Device Modeling (IC-CAP). It imports and organizes measured data and circuit netlists for various geometries and temperatures and automatically creates and efficiently simulates RF trend plots of key figures of merit based on user input.