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 (GeO2), which enables deposition of many materials onto the 2DM and subsequent heterostructure release by dissolving the GeO2 substrate. Here, we utilize this novel approach to compare how the chemistry, doping, and strain in monolayer MoS2 heterostructures fabricated by direct deposition vary from those fabricated by transfer techniques to show how interface properties differ with the heterostructure fabrication method. Direct deposition of thick Ni and Ti films is found to react with the monolayer MoS2. These interface reactions convert 50% of MoS2 into intermetallic species, which greatly exceeds the 10% conversion reported previously and 0% observed in transfer-fabricated heterostructures. We also measure notable differences in MoS2 carrier concentration depending on the heterostructure fabrication method. Direct deposition of thick Au, Ni, and Al2O3 films onto MoS2 increases the hole concentration by >10(12) cm(-2) compared to heterostructures fabricated by transferring MoS2 onto these materials. Thus, we demonstrate a universal method to fabricate 2D/3D heterostructures and expose buried interfaces for direct characterization.

Neuromorphic hardware promises to revolutionize information technology with brain-inspired parallel processing, in-memory computing, and energy-efficient implementation of artificial intelligence and machine learning. In particular, two-dimensional (2D) memtransistors enable gate-tunable non-volatile memory, bio-realistic synaptic phenomena, and atomically thin scaling. However, previously reported 2D memtransistors have not achieved low operating voltages without compromising gate-tunability. Here, we overcome this limitation by demonstrating MoS2 memtransistors with short channel lengths < 400 nm, low operating voltages < 1 V, and high field-effect switching ratios > 104 while concurrently achieving strong memristive responses. This functionality is realized by fabricating back-gated memtransistors using highly polycrystalline monolayer MoS2 channels on high-κ Al2O3 dielectric layers. Finite-element simulations confirm enhanced electrostatic modulation near the channel contacts, which reduces operating voltages without compromising memristive or field-effect switching. Overall, this work demonstrates a pathway for reducing the size and power consumption of 2D memtransistors as is required for ultrahigh-density integration.

Chemical modification is a powerful strategy for tuning the electronic properties of 2D semiconductors. Here we report the electrophilic trifluoromethylation of 2D WSe2 and MoS2 under mild conditions using the reagent trifluoromethyl thianthrenium triflate (TTT). Chemical characterization and density functional theory calculations reveal that the trifluoromethyl groups bind covalently to surface chalcogen atoms as well as oxygen substitution sites. Trifluoromethylation induces p-type doping in the underlying 2D material, enabling the modulation of charge transport and optical emission properties in WSe2. This work introduces a versatile and efficient method for tailoring the optical and electronic properties of 2D transition metal dichalcogenides.

Moire quantum materials host exotic electronic phenomena through enhanced internal Coulomb interactions in twisted two-dimensional heterostructures1-4. When combined with the exceptionally high electrostatic control in atomically thin materials5-8, moire heterostructures have the potential to enable next-generation electronic devices with unprecedented functionality. However, despite extensive exploration, moire electronic phenomena have thus far been limited to impractically low cryogenic temperatures9-14, thus precluding real-world applications of moire quantum materials. Here we report the experimental realization and room-temperature operation of a low-power (20 pW) moire synaptic transistor based on an asymmetric bilayer graphene/hexagonal boron nitride moire heterostructure. The asymmetric moire potential gives rise to robust electronic ratchet states, which enable hysteretic, non-volatile injection of charge carriers that control the conductance of the device. The asymmetric gating in dual-gated moire heterostructures realizes diverse biorealistic neuromorphic functionalities, such as reconfigurable synaptic responses, spatiotemporal-based tempotrons and Bienenstock-Cooper-Munro input-specific adaptation. In this manner, the moire synaptic transistor enables efficient compute-in-memory designs and edge hardware accelerators for artificial intelligence and machine learning. We report the experimental realization and room-temperature operation of a low-power (20 pW) moire synaptic transistor based on an asymmetric bilayer graphene/hexagonal boron nitride moire heterostructure.

Sulfur-deficient polycrystalline two-dimensional molybdenum disulfide (MoS2) transistors exhibit gate-tunable memristive properties which show promise for complex neuromorphic learning and high-performance logic and memory operations.1,2 The current understanding of the dominant switching mechanism in these ‘memtransistors’ is that the bias-induced movement of sulfur vacancies (Vs ), likely along grain boundaries (GB), changes the height of the Schottky barrier at the current-limiting contact through image-force lowering.3-5 The requirement for GB within the channel places a lower limit on the channel length (Lc ), on the order of the grain size in the MoS2. However, the source-drain voltage needed to switch between resistance states in the best-performing memtransistors4 (Lc ≈ 5 μm) is currently too large for practical memory applications. One approach to facilitate the resistance switching, without reducing Lc , would be through acceleration of the defect migration achieved through post-fabrication surface treatments applied to the channel region. The ‘all-surface’ nature of two-dimensional transistors makes surface modifications such as vapor adsorption a well-known and powerful approach for changing the electronic transport properties of MoS2,6-8 but little work has been done to explore the influence that surface treatments may have on the kinetics of the Vs movement. We have investigated two-terminal back-gated MoS2 memtransistors on 300-nm SiO2 using variable-temperature drain voltage pulse trains, complemented with in situ Raman spectroscopy in a controlled atmosphere environment, to demonstrate the effect of adsorbed molecular species on the kinetics of the resistive switching. We also examined how the electronic trap-filling processes concomitant with the resistive switching are modified by the adsorption of molecular species and how these modifications alter the kinetics of the resistive switching in turn. Atmospheric water was found to remain adsorbed to the MoS2 channel even after application of vacuum (P = 1 x 10-5 Torr) overnight, and was only successfully removed in vacuo after either heating to 100ºC or subjecting the channel to ~50 cyclic drain voltage sweeps (-30 V < Vds < +30 V). ‘Bunched’ pulse train measurements with 0 V gate bias performed before and after desorbing water reveal that two timescales are associated with the drain current evolution in the pristine (i.e., water-desorbed) film: a fast process which relaxes quickly, superimposed on a slower persistent change in resistance. The presence of water adsorbed to the MoS2 channel suppresses the fast process, which is tentatively assigned to electronic trap filling, while the slower persistent process corresponding to the long-lived resistive memory is maintained. However, the fast trap-filling process is observed for both the water-adsorbed and pristine condition when a subthreshold bias (-70 V) is applied to the back gate, suggesting that the energy of the trap state has been shifted significantly by the presence of the water. Further pulsing experiments on the pristine state using different pulse-bunch groupings indicate that when the pulses are bunched such that they do not fill the traps, the persistent memory switching is accelerated. Finally, experiments using ethanol and isopropyl alcohol vapors to identify whether tuning properties of the molecular adsorbate can potentially accelerate memtransistor switching kinetics in the technologically-promising subthreshold region are also presented. This work was funded by the Laboratory Directed Research and Development Program and performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the US Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the US Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. The views expressed in this article do not necessarily represent the views of the US Department of Energy or the United States Government. Strukov, et al. Nature 2008, 453, 80–83. Yang et al. Nat. Nanotechnol. 2013, 8, 13–24. Sangwan et al. Nat. Nanotechnol. 2015, 10, 403-406. Sangwan et al. Nature 2018, 554, 500-504. Li et al. ACS Nano 2018, 12, 9240-9252. Chen et al. J. Vac. Sci. Technol., B 2014, 32, 06FF02. Rao et al. 2D Mater. 2019, 6, 045031. Gustafson et al. J. Phys. Chem. C 2021, 125, 8712-8718.

Advances in algorithms and low-power computing hardware imply that machine learning is of potential use in off-grid medical data classification and diagnosis applications such as electrocardiogram interpretation. However, although support vector machine algorithms for electrocardiogram classification show high classification accuracy, hardware implementations for edge applications are impractical due to the complexity and substantial power consumption needed for kernel optimization when using conventional complementary metal–oxide–semiconductor circuits. Here we report reconfigurable mixed-kernel transistors based on dual-gated van der Waals heterojunctions that can generate fully tunable individual and mixed Gaussian and sigmoid functions for analogue support vector machine kernel applications. We show that the heterojunction-generated kernels can be used for arrhythmia detection from electrocardiogram signals with high classification accuracy compared with standard radial basis function kernels. The reconfigurable nature of mixed-kernel heterojunction transistors also allows for personalized detection using Bayesian optimization. A single mixed-kernel heterojunction device can generate the equivalent transfer function of a complementary metal–oxide–semiconductor circuit comprising dozens of transistors and thus provides a low-power approach for support vector machine classification applications.

Advanced Materials TechnologiesVolume 8, Issue 22 2370125 Back CoverFree Access Fully Inkjet-Printed, 2D Materials-Based Field-Effect Transistor for Water Sensing (Adv. Mater. Technol. 22/2023) Xiaoyu Sui, Xiaoyu Sui Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637 USA Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL, 60439 USA Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorSonal V. Rangnekar, Sonal V. Rangnekar Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorJaesung Lee, Jaesung Lee Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, 53706 USA Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843 USASearch for more papers by this authorStephanie E. Liu, Stephanie E. Liu Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorJulia R. Downing, Julia R. Downing Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorLindsay E. Chaney, Lindsay E. Chaney Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorXiaodong Yan, Xiaodong Yan Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorHyun-June Jang, Hyun-June Jang Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637 USA Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL, 60439 USASearch for more papers by this authorHaihui Pu, Haihui Pu Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637 USA Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL, 60439 USASearch for more papers by this authorXiaoao Shi, Xiaoao Shi Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637 USASearch for more papers by this authorShiyu Zhou, Shiyu Zhou Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, 53706 USASearch for more papers by this authorMark C. Hersam, Mark C. Hersam Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USA Department of Chemistry, Northwestern University, Evanston, IL, 60208 USA Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorJunhong Chen, Junhong Chen Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637 USA Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL, 60439 USASearch for more papers by this author Xiaoyu Sui, Xiaoyu Sui Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637 USA Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL, 60439 USA Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorSonal V. Rangnekar, Sonal V. Rangnekar Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorJaesung Lee, Jaesung Lee Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, 53706 USA Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843 USASearch for more papers by this authorStephanie E. Liu, Stephanie E. Liu Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorJulia R. Downing, Julia R. Downing Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorLindsay E. Chaney, Lindsay E. Chaney Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorXiaodong Yan, Xiaodong Yan Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorHyun-June Jang, Hyun-June Jang Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637 USA Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL, 60439 USASearch for more papers by this authorHaihui Pu, Haihui Pu Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637 USA Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL, 60439 USASearch for more papers by this authorXiaoao Shi, Xiaoao Shi Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637 USASearch for more papers by this authorShiyu Zhou, Shiyu Zhou Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, 53706 USASearch for more papers by this authorMark C. Hersam, Mark C. Hersam Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208 USA Department of Chemistry, Northwestern University, Evanston, IL, 60208 USA Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208 USASearch for more papers by this authorJunhong Chen, Junhong Chen Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637 USA Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, IL, 60439 USASearch for more papers by this author First published: 24 November 2023 https://doi.org/10.1002/admt.202370125AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Graphical Abstract 2D Materials-Based Sensors 2D nanomaterials of graphene and molybdenum disulfide are inkjet-printed onto a flexible substrate to produce a fully-printed field-effect transistor (FET) sensor, as demonstrated by Junhong Chen and co-workers in article 2301288. The sensor can be used to detect lead ions in water down to 10 nM. This work paves the way for additive nanomanufacturing of FET-based sensors and related devices using 2D nanomaterials. Volume8, Issue22November 24, 20232370125 RelatedInformation

Despite significant progress in solution-processing of 2D materials, it remains challenging to reliably print high-performance semiconducting channels that can be efficiently modulated in a field-effect transistor (FET). Herein, electrochemically exfoliated MoS2 nanosheets are inkjet-printed into ultrathin semiconducting channels, resulting in high on/off current ratios up to 10(3). The reported printing strategy is reliable and general for thin film channel fabrication even in the presence of the ubiquitous coffee-ring effect. Statistical modeling analysis on the printed pattern profiles suggests that a spaced parallel printing approach can overcome the coffee-ring effect during inkjet printing, resulting in uniform 2D flake percolation networks. The uniformity of the printed features allows the MoS2 channel to be hundreds of micrometers long, which easily accommodates the typical inkjet printing resolution of tens of micrometers, thereby enabling fully printed FETs. As a proof of concept, FET water sensors are demonstrated using printed MoS2 as the FET channel, and printed graphene as the electrodes and the sensing area. After functionalization of the sensing area, the printed water sensor shows a selective response to Pb2+ in water down to 2 ppb. This work paves the way for additive nanomanufacturing of FET-based sensors and related devices using 2D nanomaterials.

The increasing complexity of deep learning systems has pushed conventional computing technologies to their limits. While the memristor is one of the prevailing technologies for deep learning acceleration, it is only suited for classical learning layers where only two operands, namely weights and inputs, are processed simultaneously. Meanwhile, to improve the computational efficiency of deep learning for emerging applications, a variety of non-traditional layers requiring concurrent processing of many operands are becoming popular. For example, hypernetworks improve their predictive robustness by simultaneously processing weights and inputs against the application context. Two-electrode memristor grids cannot directly map emerging layers’ higher-order multiplicative neural interactions. Addressing this unmet need, we present crossbar processing using dual-gated memtransistors based on two-dimensional semiconductor MoS2. Unlike the memristor, the resistance states of memtransistors can be persistently programmed and can be actively controlled by multiple gate electrodes. Thus, the discussed memtransistor crossbar enables several advanced inference architectures beyond a conventional passive crossbar. For example, we show that sneak paths can be effectively suppressed in memtransistor crossbars, whereas they limit size scalability in a passive memristor crossbar. Similarly, exploiting gate terminals to suppress crossbar weights dynamically reduces biasing power by ∼20% in memtransistor crossbars for a fully connected layer of AlexNet. On emerging layers such as hypernetworks, collocating multiple operations within the same crossbar cells reduces operating power by ∼15× on the considered network cases.

Due to its unique gate-tunable non-volatility, the memtransistor is a promising component for low-energy neuromorphic computing. The grain boundary- and point defect-enabled resistive switching in MoS2 memtransistors suggests an inherent ionizing radiation tolerance. However, the memtransistor resilience under heavy ion irradiation has not yet been investigated. In this work, polycrystalline, monolayer MoS2 films, and memtransistors are irradiated with 48 keV Au. Fluence-dependent effects on the MoS2 lattice structure, chemical states, and memtransistor performance metrics are elucidated. When the Au fluence remains below 1013 cm−2, the memtransistor functionalities are preserved. When the Au fluence exceeds 1014 cm−2, the MoS2 is amorphized and memtransistor functionalities are lost. According to Raman spectroscopy and transmission electron microscopy, the MoS2 defect concentration increases with increasing Au fluence. X-ray photoelectron spectroscopy substantiates a significant S:Mo ratio reduction with increasing Au fluence. This work suggests that MoS2 memtransistors possess sufficient heavy ion resilience for few-year space missions.

Two-dimensional (2D) van der Waals materials have found broad utility in a diverse range of applications including electronics, optoelectronics, renewable energy, and quantum information technologies. Meanwhile, exponentially growing digital data coupled with the ubiquity of artificial intelligence algorithms have generated significant interest in edge neuromorphic computing as an alternative to centralized cloud computing. The drive to incorporate neuroscience principles into computing hardware is motivated by the low power consumption, parallel processing, and reconfigurability of the human brain. The diverse library of 2D materials with atomic-level thicknesses, exceptional electrostatic tunability, and integration versatility is particularly well-suited for realizing bio-realistic synaptic and neuronal functionality. Here, we summarize past and present work in this field and outline the frontier challenges that have not yet been overcome. We also delineate potential solutions and suggest that the neuroscience principles of criticality and synchrony have the potential to inspire breakthrough applications of 2D materials in neuronal computing networks.

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.

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 MoS2 grown on sapphire, the relative importance of the vertical field effect from the gate is enhanced, thereby heightening reconfigurability of the device response. Inspired by biological systems, gate pulses are used to modulate potentiation and depression, resulting in diverse learning curves and simplified spike-timing-dependent plasticity that facilitate unsupervised learning in simulated spiking neural networks. This capability also enables continuous learning, which is a previously underexplored cognitive concept in neuromorphic computing. Overall, this work demonstrates that the reconfigurability of memtransistors provides unique hardware accelerator opportunities for energy efficient artificial intelligence and machine learning.

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 TiO2  memristors grown in situ by atomic layer deposition (ALD) are demonstrated, where reversible Na migration underlies the resistive-switching mechanism. By employing ALD growth with an aqueous NaOH reactant in deionized water, uniform implantation of Na dopants is achieved in the crystallized TiO2  thin films at 250 °C without post-annealing. The resulting Na-doped TiO2  memristors show electroforming-free and self-rectifying resistive-switching behavior, and they are ideally suited for selectorless CAAs. Effective addressing of selectorless nodes is demonstrated via electrical measurement of individual memristors in a 6 × 6 crossbar using a read current of less than 1 µA with negligible sneak current at or below the noise level of ≈100 pA. Finally, the long-term potentiation and depression synaptic behavior from these Na-doped TiO2  memristors achieves greater than 99.1% accuracy for image-recognition tasks using a convolutional neural network based on the selectorless of crossbar arrays.