Keysight Technologies announced a new Machine Learning Toolkit as part of its Device Modeling Software Suite on January 15 2026. The toolkit automates the extraction of more than 200 model parameters, condensing them into fewer than ten key inputs and slashing development time from weeks to hours for advanced semiconductor nodes such as gate‑all‑around transistors, wide‑bandgap materials, and chiplet integration.
The accelerated workflow directly benefits Design Technology Co‑Optimization (DTCO) and System‑Technology Co‑Optimization (STCO) programs by delivering Process Design Kits (PDKs) faster and enabling tighter integration of device physics into system‑level design. Early adopters report that the toolkit reduces the time required to validate a new process node, allowing engineers to iterate designs more quickly and reduce risk in the product development cycle.
Keysight’s software business has been a growing contributor to its overall revenue mix. In the most recent quarter, the company’s total revenue rose 10% year‑over‑year, driven in part by the expanding software segment. Software offers higher margins because it relies on intellectual property and recurring licensing rather than the cost‑intensive manufacturing of test equipment. Management has highlighted that AI and machine‑learning capabilities are a key differentiator in the compact‑modeling space, citing a quote from Nilesh Kamdar, General Manager of Keysight EDA, that “AI/ML is fundamentally transforming the traditional workflows and methodologies of compact modeling.”
The launch follows a series of strategic acquisitions—Spirent, Optical Solutions Group, and PowerArtist—that broadened Keysight’s software portfolio and reinforced its focus on high‑margin, high‑growth markets. By adding the Machine Learning Toolkit, Keysight strengthens its position in the high‑speed digital and RF design arena and positions the company to capture earlier, stickier customer engagements in the semiconductor supply chain.
While the toolkit itself is a new product, it is part of a broader trend in the industry toward AI‑driven design tools. Competitors such as Cadence and Synopsys also offer AI‑enhanced modeling solutions, but Keysight’s integration of the toolkit into its existing Device Modeling Suite provides a seamless workflow for customers already using Keysight’s test and measurement equipment. The added value lies in the reduction of manual parameter extraction and the ability to generate compact models that are more accurate across a wider range of operating conditions.
The announcement was well received by the semiconductor community, with several early‑stage adopters noting that the toolkit’s speed and accuracy could shorten time‑to‑market for next‑generation chips. Although no specific market reaction data is available, the product launch aligns with Keysight’s strategy to shift toward software‑centric revenue, which has historically delivered higher margins and recurring income streams.
Overall, the Machine Learning Toolkit represents a meaningful step in Keysight’s transformation toward AI‑enabled design solutions, reinforcing its competitive moat in high‑speed digital and RF design while supporting the broader industry shift toward faster, more efficient semiconductor development cycles.
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