Keysight Applied sciences, Inc. has launched its new Digital Design Automation (EDA) software program portfolio to remodel how engineers tackle the calls for of next-generation applied sciences. Because the electronics trade races to develop superior options for 5G/6G and information middle purposes, Keysight’s suite of EDA instruments leverages AI, machine studying (ML), and Python integrations to dramatically cut back design time for complicated RF and chiplet merchandise.
Keysight’s EDA 2025 software program addresses essential challenges within the improvement lifecycle by enhancing information manipulation, integration, and management of best-in-class simulators, permitting engineers to construct environment friendly workflows seamlessly throughout a number of instruments. AI-enhanced workflows and high-performance computing additional cut back the time-to-insight, enabling engineers to maneuver from simulation to verification and compliance with higher confidence. For simulating quick digital interconnects, the software program is supplied with end-to-end part fashions and measurements that conform to digital requirements, offering an environment friendly and high-accuracy digital twin for complicated digital digital design challenges.
Core advantages of the EDA 2025 software program portfolio embrace:
Nilesh Kamdar, EDA Design & Verification Basic Supervisor at Keysight, mentioned, “AI is reworking how engineers strategy complicated design challenges. Automating historically time-intensive duties permits engineers to concentrate on innovation reasonably than repetitive refinements, leading to actual productiveness features. The muse for the sensible software of AI and ML is first having an open, interoperable workflow after which offering turn-key options tuned for particular purposes. It’s a captivating time, and AI and ML will undoubtedly be an enormous driver of design innovation sooner or later.”
Stephen Slater, Director of Product Administration at Keysight, mentioned, “With this launch, engineers can simply import information from measurements or swept simulations to coach neural community fashions, which then execute in a short time in subsequent simulations. This unlocks new prospects for abstracting and co-optimizing massive elements of the RF design collectively, dramatically accelerating the event course of.”
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