arXiv:2508.10409v3 Announce Type: replace-cross Abstract: This paper constructs a textual dataset for training large language models (LLMs) to learn analog circuit knowledge and customizes LLM training techniques. For dataset construction, high-quality textbooks are collected and decomposed into fine-grained learning nodes, which are then used to construct structured question-thinking-solution-answer (QTSA) quadruples using a multi-agent framework to capture both final answers and thought processes. The resulting dataset consists of 7.26M tokens of unlabeled data for continual pre-training (CP
The rapid advancement of large language models necessitates specialized knowledge domains for effective application, making dedicated dataset construction efforts critical at this juncture.
This development indicates a focused effort to integrate AI into deep engineering fields, potentially accelerating design and development cycles for complex physical systems like analog circuits.
LLMs can now be trained with highly structured and domain-specific knowledge in analog circuit design, enabling more sophisticated AI assistance in this field.
- · AI researchers in specialized domains
- · Analog circuit designers
- · EDA tool vendors
- · Hardware developers
- · Traditional analog design methodologies
- · Companies without AI integration strategies
Specialized LLMs become proficient in analog circuit design, accelerating R&D timelines.
The cost and time required for analog chip development decrease, leading to faster innovation in related hardware sectors.
AI-driven design automation could democratize access to advanced hardware development, fostering new waves of innovation and potentially shifting the competitive landscape.
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Read at arXiv cs.AI