As semiconductor manufacturing processes approach physical limits, the complexity and R&D costs of chip design continue to rise, highlighting the efficiency bottlenecks of traditional design methodologies. The rapid advancement of artificial intelligence (AI) has introduced novel solutions to chip design, emerging as a core focus of the industry. This paper takes the integrated application of AI and chip design as the research subject, and systematically sorts out AI application scenarios across the full chip design flow through literature review, case analysis, and comparative research. It further analyzes the development opportunities brought by AI, as well as existing technical and industrial limitations. The study shows that AI can effectively shorten chip design cycles, reduce R&D costs, and facilitate architectural innovation, but it also faces multiple challenges such as data scarcity, insufficient model interpretability, and industrial ecological barriers. Future efforts should focus on both technical optimization and ecological construction to promote deeper integration between AI and chip design.
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Zijie Deng (2026) studied this question.
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