ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model
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Abstract (arXiv)
The field of integrated circuit (IC) design is highly specialized, presenting significant barriers to entry and research and development challenges. Although large language models (LLMs) have achieved remarkable success in various domains, existing LLMs often fail to meet the specific needs of students, engineers, and researchers. Consequently, the potential of LLMs in the IC design domain remains largely unexplored. To address these issues, we introduce ChipExpert, the first open-source, instructional LLM specifically tailored for the IC design field. ChipExpert is trained on one of the current best open-source base model (Llama-3 8B). The entire training process encompasses several key stages, including data preparation, continue pre-training, instruction-guided supervised fine-tuning, preference alignment, and evaluation. In the data preparation stage, we construct multiple high-quality custom datasets through manual selection and data synthesis techniques. In the subsequent two stages, ChipExpert acquires a vast amount of IC design knowledge and learns how to respond to user queries professionally. ChipExpert also undergoes an alignment phase, using Direct Preference Optimization, to achieve a high standard of ethical performance. Finally, to mitigate the hallucinations of ChipExpert, we have developed a Retrieval-Augmented Generation (RAG) system, based on the IC design knowledge base. We also released the first IC design benchmark ChipICD-Bench, to evaluate the capabilities of LLMs across multiple IC design sub-domains. Through comprehensive experiments conducted on this benchmark, ChipExpert demonstrated a high level of expertise in IC design knowledge Question-and-Answer tasks.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 26 Jul 2024.
Builds on
16 of 22 references.
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4 more
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
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- 2025ChipMind: Retrieval-Augmented Reasoning for Long-Context Circuit Design Specifications
- 2025EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuit
- 2025White-Box Reasoning: Synergizing LLM Strategy and gm/Id Data for Automated Analog Circuit Design
- 2025CROP: Circuit Retrieval and Optimization with Parameter Guidance using LLMs
- 2025AMSbench: A Comprehensive Benchmark for Evaluating MLLM Capabilities in AMS Circuits