A software suite uses AI, ML, and Python to reduce design time and improve workflows, helping engineers address RF and chiplet challenges more efficiently.
Keysight Technologies has launched an Electronic Design Automation (EDA) software portfolio to help engineers meet the demands of next-generation technologies. As the electronics industry advances 5G/6G and data centre solutions, the company claims that its EDA tools use AI, machine learning, and Python integrations to cut design time for RF and chiplet products.
The EDA 2025 software addresses challenges in the development process by improving data manipulation, integration, and control over simulators. This allows engineers to create workflows across multiple tools. AI-enhanced workflows and computing speed up time-to-insight, helping engineers move from simulation to verification. The software also includes component models and measurements for simulating digital interconnects, offering a digital twin for digital design challenges.
The software portfolio offers several benefits. RF circuit design accelerates design cycles by enabling automatable workflows with Python integration and multi-domain simulation. The Python toolkit also allows engineers to quickly consolidate measured load pull data from different files and formats into a unified dataset, which can then be used to train AI/ML models.
In high-speed digital design, the software helps create digital twins for standard-specific SerDes designs, including UCIe chiplets, memory, USB, and PCIe, through the Advanced Design System (ADS) 2025 release. The IC-CAP 2025 release reduces model re-centring time by 10X for device modelling and characterisation, while Python integrations further streamline the modelling process.
Nilesh Kamdar, EDA Design & Verification General Manager at Keysight, said, “AI is transforming how engineers approach complex design challenges. Automating traditionally time-intensive tasks enables engineers to focus on innovation rather than repetitive refinements, resulting in real productivity gains. The foundation for the practical application of AI and ML is first having an open, interoperable workflow and then providing turn-key solutions tuned for specific applications. It’s a fascinating time, and AI and ML will undoubtedly be a huge driver of design innovation in the future.”
