# SemiKong: Curating, Training, and Evaluating A Semiconductor Industry-Specific Large Language Model

arXiv: https://arxiv.org/abs/2411.13802 · HTML: https://mlsanity.com/paper/2411.13802/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [ChipNeMo: Domain-Adapted LLMs for Chip Design](https://mlsanity.com/paper/2311.00176/) (2023)
- [ChipGPT: How far are we from natural language hardware design](https://mlsanity.com/paper/2305.14019/) (2023)
- [RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models](https://mlsanity.com/paper/2311.16543/) (2023)
- [RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution](https://mlsanity.com/paper/2312.08617/) (2023)
- [Large Language Model Agent for Hyper-Parameter Optimization](https://mlsanity.com/paper/2402.01881/) (2024)
- [Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework](https://mlsanity.com/paper/2403.11202/) (2024)
- [HDLdebugger: Streamlining HDL debugging with Large Language Models](https://mlsanity.com/paper/2403.11671/) (2024)
- [OSCaR: Object State Captioning and State Change Representation](https://mlsanity.com/paper/2402.17128/) (2024)
- [The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models](https://mlsanity.com/paper/2403.07257/) (2024)
- [ILILT: Implicit Learning of Inverse Lithography Technologies](https://mlsanity.com/paper/2405.03574/) (2024)
- [FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCs](https://mlsanity.com/paper/2405.05480/) (2024)
- [CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning](https://mlsanity.com/paper/2404.00980/) (2024)

## Built on by (most cited first, 4 of 4)

- [MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs](https://mlsanity.com/paper/2507.19525/) (2025)
- [Llamarine: Open-source Maritime Industry-specific Large Language Model](https://mlsanity.com/paper/2503.00203/) (2025)
- [Intelligent Assistants for the Semiconductor Failure Analysis with LLM-Based Planning Agents](https://mlsanity.com/paper/2506.15567/) (2025)
- [Industrial-Instruction: An End-to-End Framework for Building Instruction-Tuning and Benchmark Datasets from Industrial Technical Reports](https://mlsanity.com/paper/2608.22817/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated the potential to address some issues within the semiconductor industry. However, they are often general-purpose models that lack the specialized knowledge needed to tackle the unique challenges of this sector, such as the intricate physics and chemistry of semiconductor devices and processes. SemiKong, the first industry-specific LLM for the semiconductor domain, provides a foundation that can be used to develop tailored proprietary models. With SemiKong 1.0, we aim to develop a foundational model capable of understanding etching problems at an expert level. Our key contributions include (a) curating a comprehensive corpus of semiconductor-related texts, (b) creating a foundational model with in-depth semiconductor knowledge, and (c) introducing a framework for integrating expert knowledge, thereby advancing the evaluation process of domain-specific AI models. Through fine-tuning a pre-trained LLM using our curated dataset, we have shown that SemiKong outperforms larger, general-purpose LLMs in various semiconductor manufacturing and design tasks. Our extensive experiments underscore the importance of developing domain-specific LLMs as a foundation for company- or tool-specific proprietary models, paving the way for further research and applications in the semiconductor domain. Code and dataset will be available at https://github.com/aitomatic/semikong