Robust Chemiresistive Behavior in Conductive Polymer/MOF Composites.

Metal-organic frameworks (MOFs) are promising materials for gas sensing but are often limited to single-use detection. A hybridization strategy is demonstrated synergistically deploying conductive MOFs (cMOFs) and conductive polymers (cPs) as two complementary mixed ionic-electronic conductors in high-performing stand-alone chemiresistors. This work presents significant improvement in i) sensor recovery kinetics, ii) cycling stability, and iii) dynamic range at room temperature.

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems.

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, 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 technology, including memristive devices, theory, algorithms, architectures, and systems.

Nanosecond protonic programmable resistors for analog deep learning.

Nanoscale ionic programmable resistors for analog deep learning are 1000 times smaller than biological cells, but it is not yet clear how much faster they can be relative to neurons and synapses. Scaling analyses of ionic transport and charge-transfer reaction rates point to operation in the nonlinear regime, where extreme electric fields are present within the solid electrolyte and its interfaces. In this work, we generated silicon-compatible nanoscale protonic programmable resistors with highly desirable characteristics under extreme electric fields.

Neural Network Training With Asymmetric Crosspoint Elements.

Analog crossbar arrays comprising programmable non-volatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymmetric conductance modulation of practical resistive devices critically degrades the classification performance of networks trained with conventional algorithms. Here we first describe the fundamental reasons behind this incompatibility.

CMOS-Compatible Protonic Programmable Resistor Based on Phosphosilicate Glass Electrolyte for Analog Deep Learning.

Ion intercalation based programmable resistors have emerged as a potential next-generation technology for analog deep-learning applications. Proton, being the smallest ion, is a very promising candidate to enable devices with high modulation speed, low energy consumption, and enhanced endurance. In this work, we report on the first back-end CMOS-compatible nonvolatile protonic programmable resistor enabled by the integration of phosphosilicate glass (PSG) as the proton solid electrolyte layer.

Protonic solid-state electrochemical synapse for physical neural networks.

Physical neural networks made of analog resistive switching processors are promising platforms for analog computing. State-of-the-art resistive switches rely on either conductive filament formation or phase change. These processes suffer from poor reproducibility or high energy consumption, respectively.

In Situ Thermal Atomic Layer Etching for Sub-5 nm InGaAs Multigate MOSFETs.

Thermal atomic layer etching (ALE) was demonstrated on ternary III-V compound semiconductors. In particular, thermal ALE on InGaAs and InAlAs was achieved with sequential, self-limiting fluorination and ligand-exchange reactions using hydrogen fluoride (HF) as the fluorination reactant and dimethylaluminum chloride (DMAC) as the ligand-exchange reactant. Thermal ALE was investigated on planar surfaces and three-dimensional nanostructures.

Enhanced transport in transistor by tuning transition-metal oxide electronic states interfaced with diamond.

High electron affinity transition-metal oxides (TMOs) have gained a central role in two-dimensional (2D) electronics by enabling unprecedented surface charge doping efficiency in numerous exotic 2D solid-state semiconductors. Among them, diamond-based 2D electronics are entering a new era by using TMOs as surface acceptors instead of previous molecular-like unstable acceptors. Similarly, surface-doped diamond with TMOs has recently yielded record sheet hole concentrations (2 × 10 cm) and launched the quest for its implementation in microelectronic devices.

Nanometre-scale electronics with III-V compound semiconductors.

For 50 years the exponential rise in the power of electronics has been fuelled by an increase in the density of silicon complementary metal-oxide-semiconductor (CMOS) transistors and improvements to their logic performance. But silicon transistor scaling is now reaching its limits, threatening to end the microelectronics revolution. Attention is turning to a family of materials that is well placed to address this problem: group III-V compound semiconductors.