Optimization of Semiconductor Chip Design Using Artificial Intelligence
DOI:
https://doi.org/10.5281/zenodo.12798359ARK:
https://n2t.net/ark:/40704/JIEAS.v2n4a12Keywords:
Semiconductor Chip Design, Artificial intelligence, Machine Learning, Deep Learning, Reinforcement Learning, Performance Enhancement, Cost Reduction, Design Efficiency, Data Availability, Algorithm Complexity, Data Sharing, Quantum Computing, Nanotechnology, Transfer Learning, Explainable AI, Human-AI Collaboration, Ethical Considerations, AI integration, Semiconductor industry, Design OptimizationAbstract
The optimization of semiconductor chip design is pivotal for enhancing the performance and efficiency of modern electronic devices. With the advent of artificial intelligence (AI), significant advancements have been made in this domain. This paper explores the various AI methodologies applied in optimizing semiconductor chip design, including machine learning, deep learning, and reinforcement learning. It discusses the impact of these technologies on the design process, performance enhancement, and cost reduction. The paper also highlights the challenges and future directions in integrating AI with semiconductor chip design.
Furthermore, it examines case studies and real-world applications of AI in chip design, providing empirical evidence of the benefits and efficiencies gained through AI integration. The analysis extends to the comparison of traditional design methods versus AI-enhanced methods, showcasing the transformative potential of AI in driving innovation and overcoming current limitations in semiconductor design. By addressing both technical and economic aspects, this paper aims to present a holistic view of AI's role in revolutionizing semiconductor chip design.
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Liu, T., Cai, Q., Xu, C., Zhou, Z., Ni, F., Qiao, Y., & Yang, T. (2024). Rumor Detection with a novel graph neural network approach. arXiv Preprint arXiv:2403. 16206.
Liu, T., Cai, Q., Xu, C., Zhou, Z., Xiong, J., Qiao, Y., & Yang, T. (2024). Image Captioning in news report scenario. arXiv Preprint arXiv:2403. 16209.
Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024a). Accelerating Semi-Asynchronous Federated Learning. arXiv Preprint arXiv:2402. 10991.
Zhou, J., Liang, Z., Fang, Y., & Zhou, Z. (2024). Exploring Public Response to ChatGPT with Sentiment Analysis and Knowledge Mapping. IEEE Access.
Zhou, Z., Xu, C., Qiao, Y., Xiong, J., & Yu, J. (2024). Enhancing Equipment Health Prediction with Enhanced SMOTE-KNN. Journal of Industrial Engineering and Applied Science, 2(2), 13–20.
Zhou, Z., Xu, C., Qiao, Y., Ni, F., & Xiong, J. (2024). An Analysis of the Application of Machine Learning in Network Security. Journal of Industrial Engineering and Applied Science, 2(2), 5–12.
Zhou, Z. (2024). ADVANCES IN ARTIFICIAL INTELLIGENCE-DRIVEN COMPUTER VISION: COMPARISON AND ANALYSIS OF SEVERAL VISUALIZATION TOOLS.
Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024b). Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach. Computer Life, 12(1), 1–4.
Wang, L., Xiao, W., & Ye, S. (2019). Dynamic Multi-label Learning with Multiple New Labels. Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23--25, 2019, Proceedings, Part III 10, 421–431. Springer.
Wang, L., Fang, W., & Du, Y. (2024). Load Balancing Strategies in Heterogeneous Environments. Journal of Computer Technology and Applied Mathematics, 1(2), 10–18.
Wang, L. (2024). Low-Latency, High-Throughput Load Balancing Algorithms. Journal of Computer Technology and Applied Mathematics, 1(2), 1–9.
Wang, L. (2024). Network Load Balancing Strategies and Their Implications for Business Continuity. Academic Journal of Sociology and Management, 2(4), 8–13.
Li, W. (2024). The Impact of Apple’s Digital Design on Its Success: An Analysis of Interaction and Interface Design. Academic Journal of Sociology and Management, 2(4), 14–19.
Wu, R., Zhang, T., & Xu, F. (2024). Cross-Market Arbitrage Strategies Based on Deep Learning. Academic Journal of Sociology and Management, 2(4), 20–26.
Wu, R. (2024). Leveraging Deep Learning Techniques in High-Frequency Trading: Computational Opportunities and Mathematical Challenges. Academic Journal of Sociology and Management, 2(4), 27–34.
Wang, L. (2024). The Impact of Network Load Balancing on Organizational Efficiency and Managerial Decision-Making in Digital Enterprises. Academic Journal of Sociology and Management, 2(4), 41–48.
Chen, Q., & Wang, L. (2024). Social Response and Management of Cybersecurity Incidents. Academic Journal of Sociology and Management, 2(4), 49–56.
Song, C. (2024). Optimizing Management Strategies for Enhanced Performance and Energy Efficiency in Modern Computing Systems. Academic Journal of Sociology and Management, 2(4), 57–64.
Xie, Y., Li, J., & Wang, Q. (2018). Machine learning for logic gate optimization in semiconductor chip design. Journal of Semiconductor Technology and Science, 18(3), 245-256.
Liu, H., Zhang, Y., & Chen, L. (2019). Deep learning for layout optimization in semiconductor chip design. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 38(6), 1123-1134.
Zhang, X., Yang, J., & Liu, Q. (2020). Reinforcement learning for routing optimization in semiconductor chip design. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3825-3836.
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