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Chen G, Tang DM. Machine Learning as a "Catalyst" for Advancements in Carbon Nanotube Research. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:1688. [PMID: 39513768 PMCID: PMC11547478 DOI: 10.3390/nano14211688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2024] [Revised: 10/18/2024] [Accepted: 10/21/2024] [Indexed: 11/15/2024]
Abstract
The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent multiple complexity nature involved in their production, processing, and analysis. Recent advancements in machine learning (ML) have provided researchers with novel and powerful tools to address these challenges. This review explores the role of ML in the field of CNT research, focusing on how ML has enhanced CNT research by (1) revolutionizing CNT synthesis through the optimization of complex multivariable systems, enabling autonomous synthesis systems, and reducing reliance on conventional trial-and-error approaches; (2) improving the accuracy and efficiency of CNT characterizations; and (3) accelerating the development of CNT applications across several fields such as electronics, composites, and biomedical fields. This review concludes by offering perspectives on the future potential of integrating ML further into CNT research, highlighting its role in driving the field forward.
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Affiliation(s)
- Guohai Chen
- Nano Carbon Device Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 5, 1-1-1 Higashi, Tsukuba 305-8565, Japan
| | - Dai-Ming Tang
- Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Tsukuba 305-0044, Japan
- Institute of Pure and Applied Sciences, University of Tsukuba, Tsukuba 305-8571, Japan
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Radamson HH, Miao Y, Zhou Z, Wu Z, Kong Z, Gao J, Yang H, Ren Y, Zhang Y, Shi J, Xiang J, Cui H, Lu B, Li J, Liu J, Lin H, Xu H, Li M, Cao J, He C, Duan X, Zhao X, Su J, Du Y, Yu J, Wu Y, Jiang M, Liang D, Li B, Dong Y, Wang G. CMOS Scaling for the 5 nm Node and Beyond: Device, Process and Technology. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:837. [PMID: 38786792 PMCID: PMC11123950 DOI: 10.3390/nano14100837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024]
Abstract
After more than five decades, Moore's Law for transistors is approaching the end of the international technology roadmap of semiconductors (ITRS). The fate of complementary metal oxide semiconductor (CMOS) architecture has become increasingly unknown. In this era, 3D transistors in the form of gate-all-around (GAA) transistors are being considered as an excellent solution to scaling down beyond the 5 nm technology node, which solves the difficulties of carrier transport in the channel region which are mainly rooted in short channel effects (SCEs). In parallel to Moore, during the last two decades, transistors with a fully depleted SOI (FDSOI) design have also been processed for low-power electronics. Among all the possible designs, there are also tunneling field-effect transistors (TFETs), which offer very low power consumption and decent electrical characteristics. This review article presents new transistor designs, along with the integration of electronics and photonics, simulation methods, and continuation of CMOS process technology to the 5 nm technology node and beyond. The content highlights the innovative methods, challenges, and difficulties in device processing and design, as well as how to apply suitable metrology techniques as a tool to find out the imperfections and lattice distortions, strain status, and composition in the device structures.
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Affiliation(s)
- Henry H. Radamson
- Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China; (Z.Z.); (Y.R.); (H.L.); (J.C.); (C.H.); (X.D.); (Y.W.); (B.L.)
| | - Yuanhao Miao
- Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China; (Z.Z.); (Y.R.); (H.L.); (J.C.); (C.H.); (X.D.); (Y.W.); (B.L.)
| | - Ziwei Zhou
- Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China; (Z.Z.); (Y.R.); (H.L.); (J.C.); (C.H.); (X.D.); (Y.W.); (B.L.)
| | - Zhenhua Wu
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Zhenzhen Kong
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Jianfeng Gao
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Hong Yang
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Yuhui Ren
- Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China; (Z.Z.); (Y.R.); (H.L.); (J.C.); (C.H.); (X.D.); (Y.W.); (B.L.)
| | - Yongkui Zhang
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Jiangliu Shi
- Beijing Superstring Academy of Memory Technology, Beijing 100176, China; (J.S.); (J.X.); (M.J.); (D.L.)
| | - Jinjuan Xiang
- Beijing Superstring Academy of Memory Technology, Beijing 100176, China; (J.S.); (J.X.); (M.J.); (D.L.)
| | - Hushan Cui
- Jiangsu Leuven Instruments Co., Ltd., Xuzhou 221300, China;
| | - Bin Lu
- School of Physics and Information Engineering, Shanxi Normal University, Linfen 041004, China;
| | - Junjie Li
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Jinbiao Liu
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Hongxiao Lin
- Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China; (Z.Z.); (Y.R.); (H.L.); (J.C.); (C.H.); (X.D.); (Y.W.); (B.L.)
| | - Haoqing Xu
- Institute of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Mengfan Li
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
- Institute of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Jiaji Cao
- Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China; (Z.Z.); (Y.R.); (H.L.); (J.C.); (C.H.); (X.D.); (Y.W.); (B.L.)
| | - Chuangqi He
- Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China; (Z.Z.); (Y.R.); (H.L.); (J.C.); (C.H.); (X.D.); (Y.W.); (B.L.)
| | - Xiangyan Duan
- Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China; (Z.Z.); (Y.R.); (H.L.); (J.C.); (C.H.); (X.D.); (Y.W.); (B.L.)
| | - Xuewei Zhao
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
- Institute of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Jiale Su
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Yong Du
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Jiahan Yu
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Yuanyuan Wu
- Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China; (Z.Z.); (Y.R.); (H.L.); (J.C.); (C.H.); (X.D.); (Y.W.); (B.L.)
| | - Miao Jiang
- Beijing Superstring Academy of Memory Technology, Beijing 100176, China; (J.S.); (J.X.); (M.J.); (D.L.)
| | - Di Liang
- Beijing Superstring Academy of Memory Technology, Beijing 100176, China; (J.S.); (J.X.); (M.J.); (D.L.)
| | - Ben Li
- Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China; (Z.Z.); (Y.R.); (H.L.); (J.C.); (C.H.); (X.D.); (Y.W.); (B.L.)
| | - Yan Dong
- Key Laboratory of Microelectronics Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (Z.W.); (Z.K.); (J.G.); (H.Y.); (Y.Z.); (J.L.); (J.L.); (M.L.); (X.Z.); (J.S.); (Y.D.); (J.Y.); (Y.D.)
| | - Guilei Wang
- Beijing Superstring Academy of Memory Technology, Beijing 100176, China; (J.S.); (J.X.); (M.J.); (D.L.)
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
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Gilet V, Mabilleau G, Loumaigne M, Coic L, Vitale R, Oberlin T, de Morais Goulart JH, Dobigeon N, Ruckebusch C, Rousseau D. Superpixels meet essential spectra for fast Raman hyperspectral microimaging. OPTICS EXPRESS 2024; 32:932-948. [PMID: 38175114 DOI: 10.1364/oe.509736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 12/02/2023] [Indexed: 01/05/2024]
Abstract
In the context of spectral unmixing, essential information corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix which are indispensable to reproduce the full data matrix in a convex linear way. Essential information has recently been shown accessible on-the-fly via a decomposition of the measured spectra in the Fourier domain and has opened new perspectives for fast Raman hyperspectral microimaging. In addition, when some spatial prior is available about the sample, such as the existence of homogeneous objects in the image, further acceleration for the data acquisition procedure can be achieved by using superpixels. The expected gain in acquisition time is shown to be around three order of magnitude on simulated and real data with very limited distortions of the estimated spectrum of each object composing the images.
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Coic L, Vitale R, Moreau M, Rousseau D, de Morais Goulart JH, Dobigeon N, Ruckebusch C. Assessment of Essential Information in the Fourier Domain to Accelerate Raman Hyperspectral Microimaging. Anal Chem 2023; 95:15497-15504. [PMID: 37821082 PMCID: PMC10603605 DOI: 10.1021/acs.analchem.3c01383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 07/13/2023] [Indexed: 10/13/2023]
Abstract
In the context of multivariate curve resolution (MCR) and spectral unmixing, essential information (EI) corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix. In recent works, the assessment of EI has been revealed to be a very useful practical tool to select the most relevant spectral information before MCR analysis, key features being speed and compression ability. However, the canonical approach relies on the principal component analysis to evaluate the convex hull that encapsulates the data structure in the normalized score space. This implies that the evaluation of the essentiality of each spectrum can only be achieved after all the spectra have been acquired by the instrument. This paper proposes a new approach to extract EI in the Fourier domain (EIFD). Spectral information is transformed into Fourier coefficients, and EI is assessed from a convex hull analysis of the data point cloud in the 2D phasor plots of a few selected harmonics. Because the coordinate system of a phasor plot does not depend on the data themselves, the evaluation of the essentiality of the information carried by each spectrum can be achieved individually and independently from the others. As a result, time-consuming operations like Raman spectral imaging can be significantly accelerated exploiting a chemometric-driven (i.e., based on the EI content of a spectral pixel) procedure for data acquisition and targeted sampling. The usefulness of EIFD is shown by analyzing Raman hyperspectral microimaging data, demonstrating a potential 50-fold acceleration of Raman acquisition.
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Affiliation(s)
- Laureen Coic
- Université
Lille, CNRS, LASIRe, F-59000 Lille, France
| | | | - Myriam Moreau
- Université
Lille, CNRS, LASIRe, F-59000 Lille, France
| | - David Rousseau
- Université
d’Angers, LARIS, UMR IRHS INRA, 49000 Angers, France
| | | | - Nicolas Dobigeon
- Université
de Toulouse, IRIT/INP-ENSEEIHT, 31071 Toulouse, France
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Liu Y, Li X, Pei B, Ge L, Xiong Z, Zhang Z. Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning. MICROSYSTEMS & NANOENGINEERING 2023; 9:128. [PMID: 37829156 PMCID: PMC10564742 DOI: 10.1038/s41378-023-00587-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/09/2023] [Accepted: 08/20/2023] [Indexed: 10/14/2023]
Abstract
Scanning probe lithography (SPL) is a promising technology to fabricate high-resolution, customized and cost-effective features at the nanoscale. However, the quality of nano-fabrication, particularly the critical dimension, is significantly influenced by various SPL fabrication techniques and their corresponding process parameters. Meanwhile, the identification and measurement of nano-fabrication features are very time-consuming and subjective. To tackle these challenges, we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning (ML). Different from traditional SPL techniques that rely on manual labeling-based experimental methods, the proposed framework intelligently extracts reliable and global information for statistical analysis to fine-tune and optimize process parameters. Based on the proposed framework, we realized the processing of smaller critical dimensions through the optimization of process parameters, and performed direct-write nano-lithography on a large scale. Furthermore, data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.
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Affiliation(s)
- Yijie Liu
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084 China
- Beijing Key Laboratory of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing, 100084 China
| | - Xuexuan Li
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084 China
- Beijing Key Laboratory of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing, 100084 China
| | - Ben Pei
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084 China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084 China
- ‘Biomanufacturing and Engineering Living Systems’ Innovation International Talents Base (111 Base), Beijing, 100084 China
| | - Lin Ge
- NT-MDT Spectrum Instruments China office, Beijing, 100053 China
| | - Zhuo Xiong
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084 China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084 China
- ‘Biomanufacturing and Engineering Living Systems’ Innovation International Talents Base (111 Base), Beijing, 100084 China
| | - Zhen Zhang
- State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084 China
- Beijing Key Laboratory of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing, 100084 China
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Carbon-Related Materials: Graphene and Carbon Nanotubes in Semiconductor Applications and Design. MICROMACHINES 2022; 13:mi13081257. [PMID: 36014179 PMCID: PMC9412642 DOI: 10.3390/mi13081257] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/05/2022] [Accepted: 07/29/2022] [Indexed: 12/04/2022]
Abstract
As the scaling technology in the silicon-based semiconductor industry is approaching physical limits, it is necessary to search for proper materials to be utilized as alternatives for nanoscale devices and technologies. On the other hand, carbon-related nanomaterials have attracted so much attention from a vast variety of research and industry groups due to the outstanding electrical, optical, mechanical and thermal characteristics. Such materials have been used in a variety of devices in microelectronics. In particular, graphene and carbon nanotubes are extraordinarily favorable substances in the literature. Hence, investigation of carbon-related nanomaterials and nanostructures in different ranges of applications in science, technology and engineering is mandatory. This paper reviews the basics, advantages, drawbacks and investigates the recent progress and advances of such materials in micro and nanoelectronics, optoelectronics and biotechnology.
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