Microelectronic design verification remains a critical bottleneck in device development, traditionally mitigated by expanding verification teams and computational resources. Since the late 1990s, machine learning (ML) has been proposed to enhance verification efficiency, yet many techniques have not achieved mainstream adoption. This review, from the perspective of verification and ML practitioners, examines the application of ML in dynamic-based techniques for functional verification of microelectronic designs, and provides a starting point for those new to this interdisciplinary field. Historical trends, techniques, ML types, and evaluation baselines are analysed to understand why previous research has not been widely adopted in industry. The review highlights the application of ML, the techniques used and critically discusses their limitations and successes. Although there is a wealth of promising research, real-world adoption is hindered by challenges in comparing techniques, identifying suitable applications, and the expertise required for implementation. This review proposes that the field can progress through the creation and use of open datasets, common benchmarks, and verification targets. By establishing open evaluation criteria, industry can guide future research. Parallels with ML in software verification suggest potential for collaboration. Additionally, greater use of open-source designs and verification environments can allow more researchers from outside the hardware verification discipline to contribute to the challenge of verifying microelectronic designs.

翻译：微电子设计验证仍然是器件开发过程中的关键瓶颈，传统上通过扩大验证团队和计算资源来缓解。自20世纪90年代末以来，机器学习（ML）被提出用于提升验证效率，但许多技术尚未实现主流应用。本综述从验证与机器学习从业者的视角出发，考察了ML在基于动态技术的微电子设计功能验证中的应用，并为这一跨学科领域的新研究者提供了入门指引。通过分析历史趋势、技术方法、ML类型及评估基准，本文探讨了先前研究未能在工业界广泛应用的原因。综述重点阐述了ML的应用场景、所用技术，并批判性地讨论了其局限性与成功之处。尽管存在大量前景广阔的研究，但实际应用仍面临技术比较困难、适用场景识别不易以及实施所需专业知识等挑战。本综述提出，该领域可通过创建和使用开放数据集、通用基准测试及验证目标来推动进展。通过建立开放的评估标准，工业界可以引导未来研究方向。与软件验证中ML应用的类比表明该领域存在合作潜力。此外，更广泛地采用开源设计和验证环境，可使更多硬件验证领域之外的研究者参与到微电子设计验证的挑战中来。