How Samsung Foundry Accelerated 3nm Chip Library Development with Advanced Characterization Tools

When Samsung Foundry needed to deliver production libraries for its cutting-edge 3nm process node, the company turned to sophisticated electronic design automation solutions. The result showcased how Cadence’s latest characterization technology could dramatically compress development cycles while maintaining the precision required for next-generation semiconductors.

The Challenge: Speed Without Compromise

Developing production libraries for advanced nodes has traditionally demanded extensive computational resources and lengthy validation cycles. Engineers must characterize countless cell variations across different voltage levels, process conditions, and temperature ranges. Each iteration typically required separate runs, consuming significant time and hardware resources. For a foundry competing to bring 3nm products to market, this bottleneck threatened to delay customer enablement.

Unified Characterization Transforms the Process

The Samsung team implemented the Liberate Trio Characterization Suite, a unified platform that consolidated what previously required multiple parallel workflows. Rather than managing separate nominal characterization, statistical analysis, and validation processes, the platform unified these functions within a single integrated environment.

This consolidated approach delivered immediate benefits:

• Intelligent run optimization through machine learning algorithms reduced computational overhead on the most resource-intensive cell types while maintaining accuracy standards
• Adaptive multi-corner coverage automatically identified and tested critical process variation scenarios, eliminating unnecessary characterization runs
• Shared circuit analysis across multiple process-voltage-temperature (PVT) corners meant the system reused computations rather than duplicating effort

Runtime Savings Through Automation

The statistical characterization capabilities proved particularly transformative. Traditional approaches required engineers to manually define corner combinations and manage separate simulation batches. The Cadence platform intelligently adapted its testing strategies based on process variations, generating the necessary library validation formats automatically.

Machine learning capabilities further optimized resource allocation. Rather than characterizing every cell variant with identical computational intensity, the algorithms prioritized accuracy where it mattered most, reducing overall runtime on challenging components while maintaining specification compliance.

Cloud-Ready Architecture Enables Scaling

The suite’s cloud optimization allowed Samsung to scale characterization across large distributed computing farms. Robust job management, automated recovery capabilities, and incremental run features meant the team could leverage existing infrastructure more effectively than traditional approaches permitted.

This architectural advantage proved decisive for large library projects. By distributing workloads across multiple systems without manual intervention, Samsung reduced the wall-clock time required to complete comprehensive characterization campaigns.

Impact on Time-to-Market

Samsung’s design technology leadership noted the alignment between these efficiency gains and their strategic objectives. The ability to deliver production-ready 3nm libraries faster than competing methodologies provided meaningful competitive advantage in the race to enable customer designs.

The characterization suite demonstrated that advanced semiconductor manufacturing no longer requires choosing between speed and accuracy. Instead, intelligent automation—combined with the Liberate platform’s tightly integrated design flows—enables foundries to achieve both simultaneously.

For the semiconductor industry watching Samsung’s approach to 3nm enablement, the case illustrates how next-generation EDA tools reshape manufacturing economics and accelerate the path from process development to customer production.