2023 in Review: AI Takes Center Stage in the Eternal Quest for Innovation

The past year has raised the scope for innovation in chip design to a level unlike anything the industry has ever seen before. As the requirements from different industries and the chips that serve them become more complex, so does the design and verification process. The uptake of multi-die solutions has been considerable, and designers are becoming steadily more adept as they apply AI to their work. We have also witnessed the cloud feature more prominently to enable scale and flexibility, and lower costs.

Here is a look at the key developments in these areas and more that made 2023 such an exciting year in the electronics industry.

AI Provides New Spark for Innovation

The move toward increasing intelligence in chip design has been ramping up, and 2023 saw some key milestones. In February, Synopsys Design Space Optimization AI (DSO.ai™), our award-winning autonomous design system, marked its first 100 commercial tape-outs. The technology has accelerated the development of advanced-node chips, raised productivity, and reduced die size resource consumption for customers such as STMicroelectronics and SK hynix.

Our AI journey continued in March 2023 with the launch of Synopsys.ai™, the first full-stack AI-driven suite of electronic design automation (EDA) solutions to enable design, verification, testing, and manufacturing of the most advanced digital and analog chips. Qualcomm is one company using AI-driven verification to accelerate coverage closure, a story we told in August, while several major chipmakers expressed excitement about the potential for AI to dramatically enhance efficiency and chip performance while shrinking development timespans. As the year progressed, we continued enhancing Synopsys.ai, introducing Fab.da software that aims to make semiconductor manufacturing more efficient, amid shrinking chip sizes and a shortage of talent, with the ability to analyze petabytes of data. As a comprehensive process control solution, it uses AI and machine learning to enable faster production ramp and efficient high-volume manufacturing for advanced nodes.

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