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 WSe and MoS 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.

Direct Characterization of Buried Interfaces in 2D/3D Heterostructures Enabled by GeO 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.

Moiré synaptic transistor with room-temperature neuromorphic functionality.

Moiré quantum materials host exotic electronic phenomena through enhanced internal Coulomb interactions in twisted two-dimensional heterostructures. When combined with the exceptionally high electrostatic control in atomically thin materials, 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 temperatures, thus precluding real-world applications of moiré quantum materials.

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.

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 TiO  memristors grown in situ by atomic layer deposition (ALD) are demonstrated, where reversible Na migration underlies the resistive-switching mechanism.

Reconfigurable MoS 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 MoS grown on sapphire, the relative importance of the vertical field effect from the gate is enhanced, thereby heightening reconfigurability of the device response.