Synopsys provides full-stack big data analytics on its AI-driven EDA suite

Date: 12/09/2023
To support analytics of petabytes of IC-design-data generated during complex SoC chip manufacturing and testing of such chips with 10s billions transistors, Synopsys announced extension of its EDA suite with chip design industry’s first full-stack big data analytics solution. Vast amount of data includes heterogeneous design data types, such as timing routes, power profiles, die pass/fail reports, process control, and verification coverage metrics, are produced by EDA, testing, and IC production tools.

Synopsys EDA data analytics solution to provide AI-driven insight and optimization to drive improvements across exploration, design, manufacturing, and testing. It uses latest advances in AI to curate and operationalize magnitudes of heterogenous, multi-domain data to speed up root-cause analysis and deliver higher design productivity, PPA, and parametric/manufacturing yield and test quality.

Through AI-driven processes and procedures, the full-stack EDA suite with a big data analytics solution offers multi-domain data aggregation and curation, resulting in considerable productivity improvements and enhanced QoR. Deep data analytics help SoC chip designers to achieve more effective debug and optimization workflows. Before they have an impact on the quality and yield of the final product, IC vendors can quickly localize and fix problem regions throughout the manufacturing, test, and masking processes. Companies can enable novel use cases like knowledge assistants, preemptive and prescriptive what-if exploration, and guided issue resolution by using generative AI approaches on their data sets.

The AI-driven Synopsys EDA Data Analytics (.da) solution includes Synopsys Design.da, Synopsys Fab.da and Synopsys Silicon.da:

Synopsys Design.da to perform deep analysis of data from design execution, 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 that increase operational efficiencies and maximize product quality and fab yield.
Synopsys Silicon.da to collect petabytes of silicon monitor, diagnostic, and production test data from test equipment to improve chip production metrics, such as quality, yield, and throughput and silicon operation metrics, such as chip power and performance.

“As IC complexity grows and market windows shrink, the semiconductor industry is increasingly adopting artificial intelligence technologies to enhance the quality of results (QoR), speed verification and testing, improve fab yield, and boost productivity across multiple domains spanning the entire IC design flow,” said Sanjay Bali, vice president of Strategy and Product Management for the EDA Group at Synopsys. “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.”

“The volume of data generated during chip manufacturing and testing is massive, making big data tools essential to analyze and extract meaningful conclusions from these data sets,” said Dr. Greg Bazan, senior principal engineer at Marvell. “The Synopsys chip data analytics tool has been vital to improve the efficiency and quality of our manufacturing process. We look forward to experiencing how the benefits of Synopsys’ next-generation analytics tool can further improve our KPIs and reduce manufacturing and test costs for our next-generation products.”

“Advanced IC fabs are highly complex factories and need strong software solutions to meet production objectives,” said Youin Choung, VP at SK hynix. “We expect that Synopsys will be a key player for the solution.”

The Synopsys EDA Data Analytics Solution, including Synopsys Design.da, Synopsys Fab.da and Synopsys Silicon.da, are already available.

News Source: Synopsys