Synopsys Analytics Suite Drives Manufacturing Flow

Synopsys, Inc. has announced the extension of its™ full-stack EDA suite. Accordingly, the suite comes with a comprehensive AI-driven data analytics continuum for every stage of integrated circuit (IC) development.

The Synopsys EDA Data Analytics solution is the first of its kind in the semiconductor industry to provide AI-driven insight and optimization. Thus, driving improvements across exploration, design, manufacturing, and testing processes.

Most importantly, the solution combines the latest advances in AI to curate and operationalize magnitudes of heterogeneous, multi-domain data. In other words, accelerates root-cause analysis and achieves greater design productivity, manufacturing efficiency, and test quality.

Synopsys’ comprehensive AI-driven data analytics solution aggregates and utilizes data across IC design, test, and manufacturing flow to drive more intelligent decision-making.

Increasing Adoption for AI

AI-driven Synopsys EDA Data Analytics (.da) solution includes:

  • Synopsys Design.da to perform deep analysis of data from design execution. Thus, providing chip designers with comprehensive visibility and actionable design insights to uncover power, performance, and area (PPA) opportunities.
  • Synopsys Fab.da to store and analyze large streams of fab equipment process control data. Particularly, this increases operational efficiencies and maximizes product quality and fab yield.
  • Meanwhile, Synopsys Silicon.da collects petabytes of silicon monitor, diagnostic, and production test data from test equipment. Primarily, to improve chip production metrics, such as quality, yield, and throughput, and silicon operation metrics.

Sanjay Bali, vice president of Strategy and Product Management for the EDA Group at Synopsys said the semiconductor industry is increasingly adopting artificial intelligence technologies. This as IC complexity continues to grow and market windows shrink.

In short, AI plays key role in enhancing the quality of results (QoR), speed verification and testing, and improving fab yield. In addition, it can also boost productivity across multiple domains spanning the entire IC design flow. “With the new data analytics capabilities within the EDA suite, companies can now aggregate and leverage data across every layer of the EDA stack from architecture exploration, design, test, and manufacturing to drive improvements in PPA, yield, and engineering productivity,” said Bali.

Unlocking the Possibility Within Vast Volumes of Data

EDA, testing, and IC fabrication tools generate vast amounts of heterogeneous design data. These include timing paths, power profiles, die pass/fail reports, process control, or verification coverage metrics.

Moreover, leveraging this data is critical for improving productivity, PPA, and parametric/manufacturing yield. Extending the full-stack EDA suite with a big data analytics solution provides multi-domain data aggregation and curation through AI-driven flows and methodologies that deliver significant productivity gains with improved QoR. With deeper design insights, chip designers can achieve more effective debug and optimization workflows.

In addition, IC suppliers can rapidly localize and correct problem areas throughout mask, fabrication, and test processes before they impact product quality and yield. Companies also benefit from generative AI methods on their data sets to enable new use cases like knowledge assistants, preemptive and prescriptive what-if exploration, and guided issue resolution.