An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations.

翻译：集成电路（IC）行业在纳米尺度面临的一个显著挑战，是探索并开发能够降低因工艺变化加剧而产生的设计复杂性、并缩短芯片制造周转时间的方法。传统上用于此类任务的方法大多是手动的，因此耗时且资源密集。相比之下，人工智能的独特学习策略为处理超大规模集成（VLSI）设计与测试中复杂且数据密集的任务提供了众多令人兴奋的自动化方法。在VLSI设计与制造中应用人工智能和机器学习算法，通过自动化学习算法减少了理解和处理不同抽象层级内部及跨层级数据所需的时间和精力，进而提升了IC良率并缩短了制造周转时间。本文全面回顾了以往在VLSI设计与制造中引入的AI/ML自动化方法。此外，我们讨论了未来AI/ML在各个抽象层级应用的潜力，以期革新VLSI设计领域，实现高速、高智能且高效的实现方案。