# D2S-FLOW: Automated Parameter Extraction from Datasheets for SPICE Model Generation Using Large Language Models

arXiv: https://arxiv.org/abs/2502.16540 · HTML: https://mlsanity.com/paper/2502.16540/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 23 of 23)

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- [Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA](https://mlsanity.com/paper/2407.15353/) (2024)
- [AMSNet: Netlist Dataset for AMS Circuits](https://mlsanity.com/paper/2405.09045/) (2024)
- [Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation](https://mlsanity.com/paper/2409.03271/) (2024)
- [LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation](https://mlsanity.com/paper/2406.05250/) (2024)
- [RoSE-Opt: Robust and Efficient Analog Circuit Parameter Optimization with Knowledge-infused Reinforcement Learning](https://mlsanity.com/paper/2407.19150/) (2024)
- [An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought](https://mlsanity.com/paper/2407.15569/) (2024)
- [DocEDA: Automated Extraction and Design of Analog Circuits from Documents with Large Language Model](https://mlsanity.com/paper/2412.05301/) (2024)

## Abstract (arXiv)

In electronic design, engineers often manually search through extensive documents to retrieve component parameters required for constructing SPICE models, a process that is both labor-intensive and time-consuming. To address this challenge, we present an automated framework called D2S-FLOW that leverages large language models (LLMs) to extract electrical parameters from datasheets and generate SPICE models with high precision and efficiency, significantly reducing the need for manual intervention. Unlike traditional RAG systems, D2S-FLOW employs a workflow to enhance precision in handling unstructured documents and inconsistent naming conventions through three innovative mechanisms: Attention-Guided Document Focusing (AGDF), Hierarchical Document-Enhanced Retrieval (HDER), and Heterogeneous Named Entity Normalization (HNEN). AGDF narrows retrieval to user-selected documents, HDER utilizes document structure for precise parameter localization, and HNEN standardizes terminology via semantic inference. Experimental results demonstrate that the framework achieves an Exact Match (EM) of 0.86, an F1 score of 0.92, and an Exact Correctness (EC) of 0.96, outperforming the strongest baseline by 19.4%, 5.7%, and 13.1%, respectively. Additionally, it reduces API token consumption by 38% and minimizes the irrelevant information ratio to 4%, showcasing substantial improvements in resource efficiency. This research provides an effective automated solution for circuit design.