This research introduces a novel approach that automates the extraction of technical parameters from electronic design documentation, dramatically reducing manual effort and improving accuracy.

• Eliminates tedious manual searches through extensive technical documentation
• Leverages Chain-of-Thought reasoning to extract complex parameters more accurately
• Streamlines PySpice model construction for electronic design automation
• Reduces time and labor costs while improving simulation reliability

For engineering teams, this innovation represents a significant breakthrough in handling high-dimensional design data and meeting real-time processing demands, enabling faster and more reliable electronic design workflows.

Advanced Chain-of-Thought Reasoning for Parameter Extraction from Documents Using Large Language Models