Perkgoz Cahit
Department of Computer Engineering, Eskisehir Technical University, Eskişehir, Turkey.
PeerJ Comput Sci. 2024 Feb 21;10:e1885. doi: 10.7717/peerj-cs.1885. eCollection 2024.
In Complementary Metal-Oxide Semiconductor (CMOS) technology, scaling down has been a key strategy to improve chip performance and reduce power losses. However, challenges such as sub-threshold leakage and gate leakage, resulting from short-channel effects, contribute to an increase in distributed static power. Two-dimensional transition metal dichalcogenides (2D TMDs) emerge as potential solutions, serving as channel materials with steep sub-threshold swings and lower power consumption. However, the production and development of these 2-dimensional materials require some time-consuming tasks. In order to employ them in different fields, including chip technology, it is crucial to ensure that their production meets the required standards of quality and uniformity; in this context, deep learning techniques show significant potential.
This research introduces a transfer learning-based deep convolutional neural network (CNN) to classify chemical vapor deposition (CVD) grown molybdenum disulfide (MoS) flakes based on their uniformity or the occurrence of defects affecting electronic properties. Acquiring and labeling a sufficient number of microscope images for CNN training may not be realistic. To address this challenge, artificial images were generated using Fresnel equations to pre-train the CNN. Subsequently, accuracy was improved through fine-tuning with a limited set of real images.
The proposed transfer learning-based CNN method significantly improved all measurement metrics with respect to the ordinary CNNs. The initial CNN, trained with limited data and without transfer learning, achieved 68% average accuracy for binary classification. Through transfer learning and artificial images, the same CNN achieved 85% average accuracy, demonstrating an average increase of approximately 17%. While this study specifically focuses on MoS structures, the same methodology can be extended to other 2-dimensional materials by simply incorporating their specific parameters when generating artificial images.
在互补金属氧化物半导体（CMOS）技术中，缩小尺寸一直是提高芯片性能和降低功耗的关键策略。然而，由短沟道效应导致的亚阈值泄漏和栅极泄漏等挑战，会使分布式静态功耗增加。二维过渡金属二硫属化物（2D TMDs）作为具有陡峭亚阈值摆幅和较低功耗的沟道材料，成为潜在的解决方案。然而，这些二维材料的生产和开发需要一些耗时的任务。为了将它们应用于包括芯片技术在内的不同领域，确保其生产符合所需的质量和均匀性标准至关重要；在这种情况下，深度学习技术显示出巨大潜力。
本研究引入了一种基于迁移学习的深度卷积神经网络（CNN），以根据化学气相沉积（CVD）生长的二硫化钼（MoS）薄片的均匀性或影响电子性能的缺陷的出现情况对其进行分类。获取并标记足够数量的显微镜图像用于CNN训练可能不现实。为应对这一挑战，使用菲涅耳方程生成人工图像来预训练CNN。随后，通过使用有限的真实图像集进行微调来提高准确率。
所提出的基于迁移学习的CNN方法在所有测量指标方面相对于普通CNN有显著改进。最初的CNN在没有迁移学习的情况下用有限的数据进行训练，二元分类的平均准确率为68%。通过迁移学习和人工图像，同一CNN的平均准确率达到了85%，平均提高了约17%。虽然本研究专门关注MoS结构，但通过在生成人工图像时简单纳入其他二维材料的特定参数，相同的方法可以扩展到其他二维材料。
