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  1. Article ; Online: Direct Characterization of Buried Interfaces in 2D/3D Heterostructures Enabled by GeO

    Smyth, Christopher M / Cain, John M / Boehm, Alex / Ohlhausen, James A / Lam, Mila Nhu / Yan, Xiaodong / Liu, Stephanie E / Zeng, Thomas T / Sangwan, Vinod K / Hersam, Mark C / Chou, Stanley S / Ohta, Taisuke / Lu, Tzu-Ming

    ACS applied materials & interfaces

    2024  Volume 16, Issue 2, Page(s) 2847–2860

    Abstract: Inconsistent interface control in devices based on two-dimensional materials (2DMs) has limited technological maturation. Astounding variability of 2D/three-dimensional (2D/3D) interface properties has been reported, which has been exacerbated by the ... ...

    Abstract Inconsistent interface control in devices based on two-dimensional materials (2DMs) has limited technological maturation. Astounding variability of 2D/three-dimensional (2D/3D) interface properties has been reported, which has been exacerbated by the lack of direct investigations of buried interfaces commonly found in devices. Herein, we demonstrate a new process that enables the assembly and isolation of device-relevant heterostructures for buried interface characterization. This is achieved by implementing a water-soluble substrate (GeO
    Language English
    Publishing date 2024-01-03
    Publishing country United States
    Document type Journal Article
    ISSN 1944-8252
    ISSN (online) 1944-8252
    DOI 10.1021/acsami.3c12849
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Trifluoromethylation of 2D Transition Metal Dichalcogenides: A Mild Functionalization and Tunable p-Type Doping Method.

    Kerwin, Brendan / Liu, Stephanie E / Sadhukhan, Tumpa / Dasgupta, Anushka / Jones, Leighton O / López-Arteaga, Rafael / Zeng, Thomas T / Facchetti, Antonio / Schatz, George C / Hersam, Mark C / Marks, Tobin J

    Angewandte Chemie (International ed. in English)

    2024  , Page(s) e202403494

    Abstract: Chemical modification is a powerful strategy for tuning the electronic properties of 2D semiconductors. Here we report the electrophilic trifluoromethylation of 2D ... ...

    Abstract Chemical modification is a powerful strategy for tuning the electronic properties of 2D semiconductors. Here we report the electrophilic trifluoromethylation of 2D WSe
    Language English
    Publishing date 2024-03-29
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2011836-3
    ISSN 1521-3773 ; 1433-7851
    ISSN (online) 1521-3773
    ISSN 1433-7851
    DOI 10.1002/anie.202403494
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Moiré synaptic transistor with room-temperature neuromorphic functionality.

    Yan, Xiaodong / Zheng, Zhiren / Sangwan, Vinod K / Qian, Justin H / Wang, Xueqiao / Liu, Stephanie E / Watanabe, Kenji / Taniguchi, Takashi / Xu, Su-Yang / Jarillo-Herrero, Pablo / Ma, Qiong / Hersam, Mark C

    Nature

    2023  Volume 624, Issue 7992, Page(s) 551–556

    Abstract: Moiré quantum materials host exotic electronic phenomena through enhanced internal Coulomb interactions in twisted two-dimensional ... ...

    Abstract Moiré quantum materials host exotic electronic phenomena through enhanced internal Coulomb interactions in twisted two-dimensional heterostructures
    Language English
    Publishing date 2023-12-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/s41586-023-06791-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
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    Zs.MG 9: Show issues
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  4. Article ; Online: Sodium-Doped Titania Self-Rectifying Memristors for Crossbar Array Neuromorphic Architectures.

    Kim, Sung-Eun / Lee, Jin-Gyu / Ling, Leo / Liu, Stephanie E / Lim, Hyung-Kyu / Sangwan, Vinod K / Hersam, Mark C / Lee, Hong-Sub

    Advanced materials (Deerfield Beach, Fla.)

    2021  Volume 34, Issue 6, Page(s) e2106913

    Abstract: Memristors integrated into a crossbar-array architecture (CAA) are promising candidates for nonvolatile memory elements in artificial neural networks. However, the relatively low reliability of memristors coupled with crosstalk and sneak currents in CAAs ...

    Abstract Memristors integrated into a crossbar-array architecture (CAA) are promising candidates for nonvolatile memory elements in artificial neural networks. However, the relatively low reliability of memristors coupled with crosstalk and sneak currents in CAAs have limited the realization of the full potential of this technology. Here, high-reliability Na-doped TiO
    Language English
    Publishing date 2021-12-23
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1474949-X
    ISSN 1521-4095 ; 0935-9648
    ISSN (online) 1521-4095
    ISSN 0935-9648
    DOI 10.1002/adma.202106913
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  5. Article: Linear and Symmetric Li-Based Composite Memristors for Efficient Supervised Learning

    Kim, Su-Min / Kim, Sungkyu / Ling, Leo / Liu, Stephanie E. / Jin, Sila / Jung, Young Mee / Kim, Minjae / Park, Hyung-Ho / Sangwan, Vinod K. / Hersam, Mark C. / Lee, Hong-Sub

    ACS applied materials & interfaces. 2022 Jan. 19, v. 14, no. 4

    2022  

    Abstract: Emerging energy-efficient neuromorphic circuits are based on hardware implementation of artificial neural networks (ANNs) that employ the biomimetic functions of memristors. Specifically, crossbar array memristive architectures are able to perform ANN ... ...

    Abstract Emerging energy-efficient neuromorphic circuits are based on hardware implementation of artificial neural networks (ANNs) that employ the biomimetic functions of memristors. Specifically, crossbar array memristive architectures are able to perform ANN vector-matrix multiplication more efficiently than conventional CMOS hardware. Memristors with specific characteristics, such as ohmic behavior in all resistance states in addition to symmetric and linear long-term potentiation/depression (LTP/LTD), are required in order to fully realize these benefits. Here, we demonstrate a Li-based composite memristor (LCM) that achieves these objectives. The LCM consists of three phases: Li-doped TiO₂ as a Li reservoir, Li₄Ti₅O₁₂ as the insulating phase, and Li₇Ti₅O₁₂ as the metallic phase, where resistive switching correlates with the change in the relative fraction of the metallic and insulating phases. The LCM exhibits a symmetric and gradual resistive switching behavior for both set and reset operations during a full bias sweep cycle. This symmetric and linear weight update is uniquely enabled by the symmetric bidirectional migration of Li ions, which leads to gradual changes in the relative fraction of the metallic phase in the film. The optimized LCM in ANN simulation showed that exceptionally high accuracy in image classification is realized in fewer training steps compared to the nonlinear behavior of conventional memristors.
    Keywords biomimetics ; energy efficiency ; image analysis
    Language English
    Dates of publication 2022-0119
    Size p. 5673-5681.
    Publishing place American Chemical Society
    Document type Article
    ISSN 1944-8252
    DOI 10.1021/acsami.1c24562
    Database NAL-Catalogue (AGRICOLA)

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  6. Article ; Online: Linear and Symmetric Li-Based Composite Memristors for Efficient Supervised Learning.

    Kim, Su-Min / Kim, Sungkyu / Ling, Leo / Liu, Stephanie E / Jin, Sila / Jung, Young Mee / Kim, Minjae / Park, Hyung-Ho / Sangwan, Vinod K / Hersam, Mark C / Lee, Hong-Sub

    ACS applied materials & interfaces

    2022  Volume 14, Issue 4, Page(s) 5673–5681

    Abstract: Emerging energy-efficient neuromorphic circuits are based on hardware implementation of artificial neural networks (ANNs) that employ the biomimetic functions of memristors. Specifically, crossbar array memristive architectures are able to perform ANN ... ...

    Abstract Emerging energy-efficient neuromorphic circuits are based on hardware implementation of artificial neural networks (ANNs) that employ the biomimetic functions of memristors. Specifically, crossbar array memristive architectures are able to perform ANN vector-matrix multiplication more efficiently than conventional CMOS hardware. Memristors with specific characteristics, such as ohmic behavior in all resistance states in addition to symmetric and linear long-term potentiation/depression (LTP/LTD), are required in order to fully realize these benefits. Here, we demonstrate a Li-based composite memristor (LCM) that achieves these objectives. The LCM consists of three phases: Li-doped TiO
    Language English
    Publishing date 2022-01-19
    Publishing country United States
    Document type Journal Article
    ISSN 1944-8252
    ISSN (online) 1944-8252
    DOI 10.1021/acsami.1c24562
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Kategorien

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  7. Article ; Online: Reconfigurable MoS

    Yuan, Jiangtan / Liu, Stephanie E / Shylendra, Ahish / Gaviria Rojas, William A / Guo, Silu / Bergeron, Hadallia / Li, Shaowei / Lee, Hong-Sub / Nasrin, Shamma / Sangwan, Vinod K / Trivedi, Amit Ranjan / Hersam, Mark C

    Nano letters

    2021  Volume 21, Issue 15, Page(s) 6432–6440

    Abstract: Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Toward ... ...

    Abstract Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Toward this end, we introduce here a memtransistor with gate-tunable dynamic learning behavior. By fabricating memtransistors from monolayer MoS
    MeSH term(s) Algorithms ; Artificial Intelligence ; Computers ; Molybdenum ; Neural Networks, Computer
    Chemical Substances Molybdenum (81AH48963U)
    Language English
    Publishing date 2021-07-20
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 1530-6992
    ISSN (online) 1530-6992
    DOI 10.1021/acs.nanolett.1c00982
    Database MEDical Literature Analysis and Retrieval System OnLINE

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