Nano & AI: A Nobel Partnership
2024; American Chemical Society; Volume: 18; Issue: 47 Linguagem: Inglês
10.1021/acsnano.4c14832
ISSN1936-086X
AutoresJillian M. Buriak, Xiaodong Chen, Mathieu Salanne, Huolin L. Xin,
Tópico(s)Molecular Communication and Nanonetworks
ResumoInfoMetricsFiguresRef. ACS NanoVol 18/Issue 47Article This publication is free to access through this site. Learn More CiteCitationCitation and abstractCitation and referencesMore citation options ShareShare onFacebookX (Twitter)WeChatLinkedInRedditEmailJump toExpandCollapse EditorialNovember 26, 2024Nano & AI: A Nobel PartnershipClick to copy article linkArticle link copied!Xiaodong Chen*Xiaodong Chen*Email: [email protected]More by Xiaodong Chenhttps://orcid.org/0000-0002-9567-4328Jillian M. BuriakJillian M. BuriakMore by Jillian M. Buriakhttps://orcid.org/0000-0002-3312-1664Mathieu SalanneMathieu SalanneMore by Mathieu Salannehttps://orcid.org/0000-0002-1753-491XHuolin XinHuolin XinMore by Huolin XinOpen PDFACS NanoCite this: ACS Nano 2024, 18, 47, 32279–32282Click to copy citationCitation copied!https://pubs.acs.org/doi/10.1021/acsnano.4c14832https://doi.org/10.1021/acsnano.4c14832Published November 26, 2024 Publication History Published online 26 November 2024Published in issue 26 November 2024editorialCopyright © Published 2024 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissionsThis publication is licensed for personal use by The American Chemical Society. ACS PublicationsCopyright © Published 2024 by American Chemical SocietySubjectswhat are subjectsArticle subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article.NanodevicesNanomaterialsNanoscienceNanotechnologyPowerThis year's Nobel Prizes in Physics (1) and Chemistry (2) were both awarded to groundbreaking discoveries linked with artificial intelligence (AI). The selection of awardees and the highlighted scientific breakthroughs show how much this area of science has become foundational for today's research. The physics prize underlines the fact that although machine learning is inspired by the structure of the brain, the pioneers used tools inspired by physics to develop efficient models. (3) The chemistry prize was partly awarded for the first archetypal application of AI, AlphaFold, which has become the main platform for protein structure prediction in just a few years. (4,5) The impact of AI on scientific research extends far beyond protein folding, and it is not surprising that the scientific release from the Nobel Foundation points to the use of AI for materials development and discovery; (6) as pointed out, uses of artificial neural networks include modeling of fundamentals of materials, including the energy and dynamics of phases and interatomic forces at the molecular scale, to applications of functional materials to photovoltaics, as an example.The development of AI and AI-based tools is not a recent phenomenon; it has taken decades of research to reach the level of sophistication of widely accessible large language models such as ChatGPT4.0. Even by 1979, the first book summarizing the history of AI up to that point, entitled "Machines Who Think: A Personal Inquiry and Prospects of Artificial Intelligence", (7) was published and later updated in 2004. (8) In its description of this year's Nobel prize in physics, the Nobel Foundation points to seminal work by Warren McCulloch and Water Pitts in 1943, (6) who bridged logic and neuroscience to make the first steps toward AI. Key advances, carried out 40 years later by Hopfield, published in 1982, and by Hinton, in 1983–1985, were cited as the reasons for awarding these two scientists the 2024 Nobel Prize in physics as this work enabled the development of artificial neural networks. (6)Over the past decade, immense research contributions across various scientific disciplines have come to fruition, allowing AI to be utilized in almost any application. While AI is now maturing and developing quickly from its deep foundation of research, it is facing immense challenges to scale and improve further, including the need for development of high-performance computing hardware, building specialized databases for training, (9) and worrisome questions about massive energy and water requirements. (10,11) Nanoscience and nanotechnology offer promising solutions to these challenges. An example is the use of AI to accelerate the discovery of new nanomaterials for the capture and storage of renewable energy, which could then be applied to power AI.The scientific community must remain vigilant, promoting AI that not only does things well but does good things.Nano for AIClick to copy section linkSection link copied!Nanotechnology provides essential advancements in hardware and nanomaterials necessary for AI's computational demand. The evolution from microelectronics to nanoelectronics, particularly the reduction of field-effect transistor sizes from the microscale to the nanoscale, has provided the powerful computing capabilities required for AI. However, the projected surge in AI's power demand is staggering: from 8 TWh in 2024 to 52 TWh by 2026, and an estimated 652 TWh by 2030, which is more than 16% of the current total electricity demand in the US. (12) Notably, Microsoft plans to restart Unit 1 of Three Mile Island to power AI. (13) To meet AI's demand and high-performance and energy-efficient computing hardware, a few promising nanobased solutions have emerged.1.Quantum computing devices: Quantum computing holds the promise of exponentially increasing computing power by utilizing quantum bits (qubits) that exist in multiple states simultaneously. Nanotechnology is the enabler of creation of quantum materials and devices that enable stable and scalable qubits. (14−17) ACS Nano is at the forefront of research into quantum materials that exhibit exotic quantum properties. These materials could revolutionize quantum computing by enabling more stable and scalable qubits, leading to powerful and reliable quantum computers for AI applications. Nanofabrication techniques enable precise control at the atomic level, essential for constructing qubits with high coherence times. Advances in two-dimensional materials and topological insulators are also paving the way for the quantum computing devices.2.Neuromorphic computing devices: Neuromorphic computing aims to mimic the neural architecture of the human brain to achieve efficient computations. Developing artificial neurons based on chemical or electric devices requires nanoscale fabrication to achieve high speeds and low power consumption. (18) Recent advances include memristive devices that emulate synaptic functions, enabling hardware implementations of neural networks. (19−21)3.3D architectures: Moving beyond the traditional 2D chip design, 3D architectures utilizing nanomaterials allow for higher transistor density and shorter interconnects, which in turn boosts computing performance and efficiency. (22,23)4.Nanosensors for data acquisition: AI thrives on data. Nanotechnology enables the development of highly sensitive and selective sensors that can gather vast amounts of data from the environment and importantly, directly from humans. This aspect comprises the research areas of wearable nanosensors, implantable nanosensors, nanosensors for brain–computer interfaces, and so on. (24) These sensor arrays generate rich data sets essential for training and improving AI algorithms, particularly in personalized medicine and human–machine interface.AI for NanoClick to copy section linkSection link copied!AI serves as a powerful paradigm for accelerating nano research and discovery. The vast amount of data generated from the research of nanoscience and nanotechnology, spanning physics, chemistry, materials science, biology, medicine and electronics, provides data sets essential for developing AI algorithms tailored to nano applications. Due to AI's capabilities in autonomous identification, data-driven modeling, and constrained optimization (analogous to characterization, physics-based modeling and optimization in materials research), it has promising applications.Nano and AI are increasingly intertwined, forming a Nobel partnership that holds immense promise for the future.1.Nanomaterials discovery: Combining advances in AI with robotics can revolutionize the discovery of new nanomaterials through the rise of automated laboratories. This approach relies on the integration of tools such as high-throughput virtual screening, automated synthesis planning, and machine-learning algorithms that are able to direct experiments and interpret results on-the-fly to design new procedures. Self-driving laboratories comprise intelligent robotic laboratory assistants that dramatically speed up the rate of lab-based discovery via rapid exploration of chemical space in a closed-loop format. (25,26) Their utility for the discovery and optimization of nanomaterials using both experimental approaches and simulations (27) is enormous.2.Nano characterization: AI enhances the accuracy of identifying nanoscale phenomena. Deep-learning models can be trained to support many analyses, including high-precision atom segmentation, localization, denoising, and super-resolving of atomic-resolution images recorded by TEM (28−30) identifying chemical features and decomposing their oxidation states using electron energy loss, X-ray absorption, and Raman spectroscopy, (31−35) inpainting the missing wedge in electron tomography, breaking the 0.7 Å 3D imaging barrier and enabling low-dose imaging and quantitative analysis, (36−40) and phase identification at the nano and atomic scales. (41−43)3.Structure–property relationships: Predicting the chemical and physical properties of a molecule from only its structure has long been an inaccessible dream for many chemists. In future years, it may become reachable, even in the case of complex nanomaterials, through the use of advanced AI models that have already shown their ability to efficiently learn correlations between variables. (44)4.Chemical sensing and disease screening: AI enables the automatic identification of targets with high precision. Nanosensors combined with AI algorithms improve the detection of biomarkers for diseases, environmental pollutants, and chemical threats. (45)Despite the vast potential of AI, the scarcity of comprehensive, classified, and formatted databases comprising historical and up-to-date research in nanoscience hinders the full potential of AI applications. It is imperative for the nano community to work together and build and share such databases, enabling AI to extract valuable insights and facilitate evidence-based decision-making.As AI becomes more integrated into nanoscience and technology, it is crucial to address safety and ethical issues. Like biotechnology, which requires oversight through the Institutional Review Board (IRB), AI applications must be scrutinized for potential risks, such as data privacy, algorithmic bias, transparency and explainability, autonomy and control, and so on. Regulations and guidelines need to be established to ensure responsible development and deployment of AI in nanoscience and nanotechnology. The scientific community must remain vigilant, promoting AI that not only does things well but does good things.In short, nano and AI are increasingly intertwined, forming a Nobel partnership that holds immense promise for the future. The interdisciplinary integration (one of gene at ACS Nano) holds the promise of significant advancements and may give rise to a new discipline, offering vast opportunities for scientists from all fields.AnnouncementsClick to copy section linkSection link copied!Nominations are open for the 2025 ACS Nano Lectureship, which is to honor two outstanding early career investigators who have made substantial impacts in the areas of nanoscience and nanotechnology. The nomination deadline is December 31, 2024.Nominations are also open for the 2025 ACS Nano Impact Award, which aims to honor a team of authors for their significant contributions to research work published in ACS Nano during the two calendar years immediately preceding the award year. The nomination deadline is December 31, 2024.We are pleased to announce the appointment of our new Associate Editors at ACS Nano, Prof. Yang Chai and Prof. Maria Lukatskaya (Figure 1). Prof. Chai, who serves as Professor of Applied Physics at the Hong Kong Polytechnic University, brings a wealth of knowledge in semiconductor physics, field-effect transistors, emerging memories, intelligent sensors, in-sensor and in-memory computing, 2D materials, and flexible/wearable electronics. Prof. Lukatskaya, an Assistant Professor in Department of Mechanical and Process Engineering at ETH Zürich, brings expertise in development of next-generation battery technologies and CO2 capture/utilization methods.Figure 1Figure 1. Prof. Yang Chai (left) and Prof Maria Lukatskaya (right) were appointed as Associate Editors of ACS Nano since September 2024. Photograph courtesy of Yang Chai and Maria Lukatskaya.High Resolution ImageDownload MS PowerPoint SlideAuthor InformationClick to copy section linkSection link copied!Corresponding AuthorXiaodong Chen, Editor-in-Chief, https://orcid.org/0000-0002-9567-4328, Email: [email protected]AuthorsJillian M. Buriak, Executive Editor, https://orcid.org/0000-0002-3312-1664Mathieu Salanne, Associate Editor, https://orcid.org/0000-0002-1753-491XHuolin Xin, Associate EditorNotesViews expressed in this editorial are those of the authors and not necessarily the views of the ACS.ReferencesClick to copy section linkSection link copied! This article references 45 other publications. 1https://www.nobelprize.org/prizes/physics/2024/press-release/.Google ScholarThere is no corresponding record for this reference.2https://www.nobelprize.org/prizes/chemistry/2024/press-release/.Google ScholarThere is no corresponding record for this reference.3https://www.nobelprize.org/prizes/physics/2024/summary/.Google ScholarThere is no corresponding record for this reference.4https://www.nobelprize.org/prizes/chemistry/2024/summary/.Google ScholarThere is no corresponding record for this reference.5https://deepmind.google/technologies/alphafold/.Google ScholarThere is no corresponding record for this reference.6https://www.nobelprize.org/uploads/2024/09/advanced-physicsprize2024.pdf.Google ScholarThere is no corresponding record for this reference.7McCorduck, P. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, 2nd ed.; A K Peters/CRC Press, 2004. DOI: 10.1201/9780429258985 .Google ScholarThere is no corresponding record for this reference.8Casti, J. L. Synthetic Thought. Nature 2004, 427, 680, DOI: 10.1038/427680a Google ScholarThere is no corresponding record for this reference.9Filinger, W.; Mullen, J.; Cohen, J.; Wittke, S.; Backhaus, A. Building HPC Learning Pathways: Understanding our Community, PEARC '24: Practice and Experience in Advanced Research Computing 2024. Human Powered Computing 2024, 32, 1– 7, DOI: 10.1145/3626203.3670513 Google ScholarThere is no corresponding record for this reference.10https://www.forbes.com/sites/arielcohen/2024/05/23/ai-is-pushing-the-world-towards-an-energy-crisis/.Google ScholarThere is no corresponding record for this reference.11https://www.wired.com/story/ai-energy-demands-water-impact-internet-hyper-consumption-era/.Google ScholarThere is no corresponding record for this reference.12https://www.forbes.com/sites/bethkindig/2024/06/20/ai-power-consumption-rapidly-becoming-mission-critical/.Google ScholarThere is no corresponding record for this reference.13https://www.nbcnews.com/business/business-news/three-mile-island-nuclear-plant-help-power-microsoft-data-center-needs-rcna171958.Google ScholarThere is no corresponding record for this reference.14Tranter, A. D.; Kranz, L.; Sutherland, S.; Keizer, J. G.; Gorman, S. K.; Buchler, B. C.; Simmons, M. Y. Machine Learning-Assisted Precision Manufacturing of Atom Qubits in Silicon. ACS Nano 2024, 18, 19489– 19497, DOI: 10.1021/acsnano.4c00080 Google ScholarThere is no corresponding record for this reference.15Jones, M. T.; Monir, M. S.; Krauth, F. N.; Macha, P.; Hsueh, Y. L. Atomic Engineering of Molecular Qubits for High-Speed, High-Fidelity Single Qubit Gates. ACS Nano 2023, 17, 22601– 22610, DOI: 10.1021/acsnano.3c06668 Google ScholarThere is no corresponding record for this reference.16Oh, J. S.; Zaman, R.; Murthy, A. A.; Bal, M.; Crisa, F.; Zhu, S. Structure and Formation Mechanisms in Tantalum and Niobium Oxides in Superconducting Quantum Circuits. ACS Nano 2024, 18, 19732– 19741, DOI: 10.1021/acsnano.4c05251 Google ScholarThere is no corresponding record for this reference.17Mun, J.; Sushko, P. V.; Brass, E.; Zhou, C.; Kisslinger, K.; Qu, X.; Liu, M.; Zhu, Y. Probing Oxidation-Driven Amorphized Surfaces in a Ta(110) Film for Superconducting Qubit. ACS Nano 2024, 18, 1126– 1136, DOI: 10.1021/acsnano.3c10740 Google Scholar17Probing Oxidation-Driven Amorphized Surfaces in a Ta(110) Film for Superconducting QubitMun, Junsik; Sushko, Peter V.; Brass, Emma; Zhou, Chenyu; Kisslinger, Kim; Qu, Xiaohui; Liu, Mingzhao; Zhu, YimeiACS Nano (2024), 18 (1), 1126-1136CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society) Recent advances in superconducting qubit technol. have led to significant progress in quantum computing, but the challenge of achieving a long coherence time remains. Despite the excellent lifetime performance that tantalum (Ta) based qubits have demonstrated to date, the majority of superconducting qubit systems, including Ta-based qubits, are generally believed to have uncontrolled surface oxidn. as the primary source of the two-level system loss in two-dimensional transmon qubits. Therefore, at.-scale insight into the surface oxidn. process is needed to make progress toward a practical quantum processor. In this study, the surface oxidn. mechanism of native Ta films and its potential impact on the lifetime of superconducting qubits were investigated using advanced scanning transmission electron microscopy (STEM) techniques combined with d. functional theory calcns. The results suggest an atomistic model of the oxidized Ta(110) surface, showing that oxygen atoms tend to penetrate the Ta surface and accumulate between the two outermost Ta at. planes; oxygen accumulation at the level exceeding a 1:1 O/Ta ratio drives disordering and, eventually, the formation of an amorphous Ta2O5 phase. In addn., we discuss how the formation of a noninsulating ordered TaO1-δ (δ < 0.1) suboxide layer could further contribute to the losses of superconducting qubits. Subsurface oxidn. leads to charge redistribution and elec. polarization, potentially causing quasiparticle loss and decreased current-carrying capacity, thus affecting superconducting qubit coherence. The findings enhance the comprehension of the realistic factors that might influence the performance of superconducting qubits, thus providing valuable guidance for the development of future quantum computing hardware. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXis1Oktb7M&md5=e9d74e4ace93a89d6764cab24160a3b718He, K.; Wang, C.; He, Y.; Su, J.; Chen, X. Artificial Neuron Devices. Chem. Rev. 2023, 123, 13796– 13865, DOI: 10.1021/acs.chemrev.3c00527 Google ScholarThere is no corresponding record for this reference.19Song, M. K.; Kang, J.-H.; Zhang, X.; Ji, W.; Ascoli, A. Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems. ACS Nano 2023, 17, 11994– 12039, DOI: 10.1021/acsnano.3c03505 Google Scholar19Recent Advances and Future Prospects for Memristive Materials, Devices, and SystemsSong, Min-Kyu; Kang, Ji-Hoon; Zhang, Xinyuan; Ji, Wonjae; Ascoli, Alon; Messaris, Ioannis; Demirkol, Ahmet Samil; Dong, Bowei; Aggarwal, Samarth; Wan, Weier; Hong, Seok-Man; Cardwell, Suma George; Boybat, Irem; Seo, Jae-sun; Lee, Jang-Sik; Lanza, Mario; Yeon, Hanwool; Onen, Murat; Li, Ju; Yildiz, Bilge; del Alamo, Jesus A.; Kim, Seyoung; Choi, Shinhyun; Milano, Gianluca; Ricciardi, Carlo; Alff, Lambert; Chai, Yang; Wang, Zhongrui; Bhaskaran, Harish; Hersam, Mark C.; Strukov, Dmitri; Wong, H.-S. Philip; Valov, Ilia; Gao, Bin; Wu, Huaqiang; Tetzlaff, Ronald; Sebastian, Abu; Lu, Wei; Chua, Leon; Yang, J. Joshua; Kim, JeehwanACS Nano (2023), 17 (13), 11994-12039CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society) A review. Memristive technol. has been rapidly emerging as a potential alternative to traditional CMOS technol., which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technol., including memristive devices, theory, algorithms, architectures, and systems. In addn., we discuss research directions for various applications of memristive technol. including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technol., outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technol., this review aims to inform and inspire further research in this field. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXhtlart7bO&md5=d1ba23909f0d68e5e94192599fe5481620Lewerenz, M.; Passerini, E.; Cheng, B.; Fischer, M.; Emboras, A.; Luisier, M.; Koch, U.; Leuthold, J. Versatile Nanoscale Three-Terminal Memristive Switch Enabled by Gating. ACS Nano 2024, 18, 10798– 10806, DOI: 10.1021/acsnano.3c11373 Google ScholarThere is no corresponding record for this reference.21Gamage, S.; Manna, S.; Zajac, M.; Hancock, S.; Wang, Q. Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Memristive Devices. ACS Nano 2024, 18, 2105– 2116, DOI: 10.1021/acsnano.3c09281 Google Scholar21Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Memristive DevicesGamage, Sampath; Manna, Sukriti; Zajac, Marc; Hancock, Steven; Wang, Qi; Singh, Sarabpreet; Ghafariasl, Mahdi; Yao, Kun; Tiwald, Tom E.; Park, Tae Joon; Landau, David P.; Wen, Haidan; Sankaranarayanan, Subramanian K. R. S.; Darancet, Pierre; Ramanathan, Shriram; Abate, YohannesACS Nano (2024), 18 (3), 2105-2116CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society) Solid-state devices made from correlated oxides, such as perovskite nickelates, are promising for neuromorphic computing by mimicking biol. synaptic function. However, comprehending dopant action at the nanoscale poses a formidable challenge to understanding the elementary mechanisms involved. Here, we perform operando IR nanoimaging of hydrogen-doped correlated perovskite, neodymium nickel oxide (H-NdNiO3, H-NNO), devices and reveal how an applied field perturbs dopant distribution at the nanoscale. This perturbation leads to stripe phases of varying cond. perpendicular to the applied field, which define the macroscale elec. characteristics of the devices. Hyperspectral nano-FTIR imaging in conjunction with d. functional theory calcns. unveils a real-space map of multiple vibrational states of H-NNO assocd. with OH stretching modes and their dependence on the dopant concn. Moreover, the localization of excess charges induces an out-of-plane lattice expansion in NNO which was confirmed by in situ X-ray diffraction and creates a strain that acts as a barrier against further diffusion. Our results and the techniques presented here hold great potential for the rapidly growing field of memristors and neuromorphic devices wherein nanoscale ion motion is fundamentally responsible for function. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB2cXnsVCnug%253D%253D&md5=1a59aa7afd8e4df5d6e583a73b2134a422Kim, J.-y.; Ju, X.; Ang, K.-W.; Chi, D. Van der Waals Layer Transfer of 2D Materials for Monolithic 3D Electronic System Integration: Review and Outlook. ACS Nano 2023, 17, 1831– 1844, DOI: 10.1021/acsnano.2c10737 Google Scholar22Van der Waals Layer Transfer of 2D Materials for Monolithic 3D Electronic System Integration: Review and OutlookKim, Jun-young; Ju, Xin; Ang, Kah-Wee; Chi, DongzhiACS Nano (2023), 17 (3), 1831-1844CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society) A review. Two-dimensional materials (2DMs) have attracted a great deal of interest due to their immense potential for scientific breakthroughs and technol. innovations. While some 2D transition metal dichalcogenides (TMDC) such as MoS2 and WS2 are considered as the ultimate channel materials in unltrascaled transistors as replacements for Si, there has also been increasing interest in the monolithic 3D integration of 2DMs on the Si CMOS platform or in flexible electronics as back-end-of-line transistors, memory devices/selectors, and sensors, taking advantage of 2DM properties such as a high current driving capability with low leakage current, nonvolatile switching characteristics, a large surface-to-vol. ratio, and a tunable bandgap. However, the realization of both of these scenarios critically depends on the development of manufg.-viable high-yield 2DM layers transfer from the growth substrate to the Si, since the growth of high-quality 2DM layers often requires a high-temp. growth process on template substrates. Motivated by this, extensive efforts have been made by the 2DM research community to develop various 2DM layer transfer methods, leveraging the van der Waals transfer capability of the layer-structured 2DMs. These efforts have led to a no. of successful demonstrations of wafer-scale 2D TMDC layer transfer, while 2DM-enabled template growth/transfer of some functional bulk materials such as III-V, Ge, and AlN has also been demonstrated. This review surveys and compares different 2DM transfer methods developed recently, with a focus on large-area 2D TMDC film transfer along with an introduction of 2DM template-assisted van der Waals growth/transfer of non-2D thin films. We will also briefly present an outlook of our envisioned multifunctionalities in 3D integrated electronic systems enabled by monolithic 3D integration of 2DMs and III-V via van der Waals transfer and discuss possible technol. options for overcoming remaining challenges. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXht12rt78%253D&md5=c2b690ea38e356b8c291fcc9c7e4abd523Fan, C.; Cheng, X.; Xie, Y.; Liu, F.; Deng, X.; Zhu, M.; Gao, Y.; Xiao, M.; Zhang, Z. Monolithic Three-Dimensional Integration of Carbon Nanotube Circuits and Sensors for Smart Sensing Chips. ACS Nano 2023, 17, 10987– 10995, DOI: 10.1021/acsnano.3c03190 Google Scholar23Monolithic Three-Dimensional Integration of Carbon Nanotube Circuits and Sensors for Smart Sensing ChipsFan, Chenwei; Cheng, Xiaohan; Xie, Yunong; Liu, Fangfang; Deng, Xiaosong; Zhu, Maguang; Gao, Yunfei; Xiao, Mengmeng; Zhang, ZhiyongACS Nano (2023), 17 (11), 10987-10995CODEN: ANCAC3; ISSN:1936-0851. (American Chemical Society) Semiconducting carbon nanotube (CNT) film is a promising material for constructing high-performance complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs) and highly sensitive field-effect transistor (FET) bio/chem. sensors. Moreover, CNT logic transistors and sensors can be integrated through a compatible low-temp. fabrication process, providing enough thermal budget to construct monolithic three-dimensional (M3D) systems for smart sensors. However, an M3D sensing chip based on CNT film has not yet been demonstrated. In this work, we develop M3D technol. to fabricate CNT CMOS ICs and CNT sensor arrays in two different layers; then, we demonstrate a preliminary M3D sensing system comprising CNT CMOS interfacing ICs in the bottom layer and CNT sensors in the upper layer through interlayer vias as links. As a typical example, a highly sensitive hydrogen sensing IC has been demonstrated to perform in situ sensing and processing functions through upper-layer FET-based hydrogen sensors exposed to the environment and bottom-layer CNT CMOS voltage-controlled oscillator (VCO) interfacing circuits. The M3D CNT sensing ICs convert hydrogen concn. information (8-128 ppm) to digital frequency information (0.78-1.11 GHz) with a sensitivity of 2.75 MHz/ppm. M3D sensing technol. is expected to provide a universal sensing system for future smart sensing chips, including multitarget detection and ultralow power sensors. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3sXhtFalsL7M&md5=b3323ce1735c522d3cad1b7fd9b1c1ed24Zheng, J.;
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