Engineering Data Management for AI-Ready Semiconductor Design

Keysight Technologies has introduced SOS Enterprise, an advanced engineering data management platform aimed at addressing one of the principal barriers to large-scale artificial intelligence (AI) integration in semiconductor design: fragmented and poorly governed engineering data. SOS Enterprise targets complex electronics and semiconductor development environments where design information is distributed across multiple teams, tools, and storage systems, impeding traceability, compliance, and reuse of critical assets such as intellectual property (IP) and verification artifacts.

Engineering Data Complexity in Semiconductor Development
As semiconductor design cycles accelerate, the volume and diversity of engineering information produced during a project—ranging from requirements and RTL code to simulation results and verification data—has grown substantially. In large organizations operating across multiple sites, this data often resides in disparate repositories and file systems. Without structured governance and traceability, engineers spend considerable time retrieving correct versions of design files and resolving inconsistencies, a workflow that delays development and undermines confidence in downstream automation tools including AI. Fragmented data also complicates compliance with regulatory frameworks and industry standards, where audit trails and demonstrable version control are mandatory.

AI and machine learning models used in design automation and analysis require well-organized data with explicit lineage and metadata to produce reliable outputs. When data lacks clear structure, traceability, or standardization, the effectiveness of AI integrations is limited. Keysight positions SOS Enterprise as a solution to this foundational issue by treating design files, verification results, and IP libraries as versioned, traceable assets within a single system of record.

SOS Enterprise: Unified Governance and Traceability
SOS Enterprise extends Keysight’s existing engineering data management capabilities with enterprise-grade automation for governance and traceability. At its core, the platform consolidates engineering information into a governed repository, establishing clear data lineage and standardized information management across distributed teams and regulatory environments. This consolidation is intended to transform siloed design data into reusable knowledge, enabling consistent access to validated IP and design assets regardless of physical location.

The platform automates several critical functions that historically required manual effort. Automated compliance tracking generates audit trails and supports regulatory requirements without increasing engineering workload. Role-based access control and geofencing enhance data security by enforcing fine-grained permissions across regions and teams. By automating custom workflows and traceability throughout the design lifecycle, SOS Enterprise reduces reliance on manual processes and creates a foundation for integrating AI into design and verification flows.

Application Areas and Technical Benefits
SOS Enterprise is relevant across industries where complex semiconductor and electronic systems are developed—most notably aerospace and defense, automotive, and high-performance computing sectors that must balance rapid innovation with stringent compliance and security requirements. Within such environments, the platform’s standardized engineering information management can accelerate time to insight, support regulatory audits, and improve operational efficiency.

One tangible benefit of systematized engineering data is improved IP reuse. By enabling engineers to locate and leverage validated components and design blocks from a central repository, SOS Enterprise can reduce redundant development effort and shorten project timelines. Additionally, automated traceability supports more robust verification processes by preserving the context and relationships among design artifacts, which is crucial for regression analysis and certification workflows.

Preparing for AI Integration in Design Workflows
The platform’s emphasis on data governance and traceability directly supports the broader adoption of AI in semiconductor design. Well-structured, versioned engineering data with clear lineage is a prerequisite for many machine learning and AI tools to function effectively at enterprise scale. By standardizing the underlying information infrastructure and eliminating data silos, SOS Enterprise prepares engineering organizations to integrate advanced analytics, AI-assisted automation, and intelligent design exploration into their workflows with greater confidence in the reliability of results.

www.keysight.com