LEVERAGING SKILL CODING FOR AUTOMATION IN ANALOG LAYOUT DESIGN

Kavya Gaddipati

doi:10.34218/IJCET_16_01_184

Authors

Kavya Gaddipati Arizona State University, USA. Author

DOI:

https://doi.org/10.34218/IJCET_16_01_184

Keywords:

Analog Layout Automation, SKILL Programming, Machine Learning Integration, Layout-Dependent Effects, Design Optimization

Abstract

SKILL coding has revolutionized analog layout design automation by bridging the gap between manual craftsmanship and computational efficiency. The integration of SKILL-based automation with human expertise offers sophisticated solutions for addressing complex challenges in advanced technology nodes, particularly in managing layout-dependent effects and device matching requirements. This advancement enables systematic approaches to automation while preserving the critical role of expert judgment in analog design. Through machine learning integration and standardized methodologies, SKILL automation enhances design quality, reduces iteration cycles, and improves manufacturing yield. The framework provides comprehensive solutions for parameterized cell generation, custom automation tools, and verification processes, significantly impacting the efficiency of analog design workflows. As semiconductor technologies advance toward smaller nodes, the synergy between SKILL automation and artificial intelligence continues to evolve, offering enhanced capabilities in pattern recognition, optimization strategies, and predictive analytics. The implementation of these automated solutions demonstrates substantial improvements in design cycle time, first-pass success rates, and overall quality metrics while maintaining the precision required for analog circuit design.

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