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

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.

Direct Characterization of Buried Interfaces in 2D/3D Heterostructures Enabled by GeO2 Release Layer.

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 >1012 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.

Moiré synaptic transistor with room-temperature neuromorphic functionality.

Moiré 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, moiré heterostructures have the potential to enable next-generation electronic devices with unprecedented functionality. However, despite extensive exploration, moiré electronic phenomena have thus far been limited to impractically low cryogenic temperatures9-14, thus precluding real-world applications of moiré quantum materials. Here we report the experimental realization and room-temperature operation of a low-power (20 pW) moiré synaptic transistor based on an asymmetric bilayer graphene/hexagonal boron nitride moiré heterostructure. The asymmetric moiré 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 moiré 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 moiré synaptic transistor enables efficient compute-in-memory designs and edge hardware accelerators for artificial intelligence and machine learning.

Linear and Symmetric Li-Based Composite Memristors for Efficient Supervised Learning.

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.

Sodium-Doped Titania Self-Rectifying Memristors for Crossbar Array Neuromorphic Architectures.

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.

Reconfigurable MoS2 Memtransistors for Continuous Learning in Spiking Neural Networks.

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.