Presentation
AI-Based Trimming and Optimization for Voltage Regulators: Proven Accuracy with Wafer Data
DescriptionIn this case study, we present an innovative AI-based trimming and optimization methodology developed for voltage regulators. Traditional methods of optimizing analog circuits, such as voltage references and regulators, are time-consuming and often result in suboptimal performance due to manual trimming processes. Our approach leverages AI-powered Solido Design Environment to automate the trimming process, significantly reducing the 3-sigma range of output voltages and achieving a remarkable 93% accuracy in wafer measurement data.
The methodology involves using automated trimming technique to find optimal trimming codes for target voltages, followed by circuit optimization to provide the best netlist. This process not only enhances the accuracy of the output voltage but also accelerates the optimization process by 140 times compared to manual methods. The results demonstrate a substantial improvement in the performance of voltage regulators, making this approach a valuable contribution to the field of analog circuit design.
Key findings include:
• Automated trimming code identification for voltage references and regulators.
• Significant reduction in the 3-sigma range of output voltages.
• Achieving 93% accuracy in wafer measurement data.
• 140x speed improvement in the optimization process.
This case study highlights the potential of AI-driven methodologies to revolutionize analog circuit design, offering both enhanced performance and efficiency.
The methodology involves using automated trimming technique to find optimal trimming codes for target voltages, followed by circuit optimization to provide the best netlist. This process not only enhances the accuracy of the output voltage but also accelerates the optimization process by 140 times compared to manual methods. The results demonstrate a substantial improvement in the performance of voltage regulators, making this approach a valuable contribution to the field of analog circuit design.
Key findings include:
• Automated trimming code identification for voltage references and regulators.
• Significant reduction in the 3-sigma range of output voltages.
• Achieving 93% accuracy in wafer measurement data.
• 140x speed improvement in the optimization process.
This case study highlights the potential of AI-driven methodologies to revolutionize analog circuit design, offering both enhanced performance and efficiency.
Event Type
Engineering Poster
TimeTuesday, June 245:00pm - 6:00pm PDT
LocationEngineering Posters, Level 2 Exhibit Hall