Directly drawn top-gate semiconducting carbon nanotube thin-film transistors and complementary inverters.

As the emerging demand for electronic devices that are simple, cost effective and capable of rapid fabrication has increased, novel fabrication techniques for designing and manufacturing such devices have attracted remarkable research interest. One method for prototyping these electronic devices is to draw them using a handwriting tool that is commonly available. In this work, we demonstrate a transistor and complementary logic inverter that are directly drawn using a brush and that are based on solution-based materials such as semiconducting carbon nanotubes (CNTs), silver ink and paste, and cross-linked poly(4-vinylphenol) (cPVP).

Binarized Neural Network with Silicon Nanosheet Synaptic Transistors for Supervised Pattern Classification.

In the biological neural network, the learning process is achieved through massively parallel synaptic connections between neurons that can be adjusted in an analog manner. Recent developments in emerging synaptic devices and their networks can emulate the functionality of a biological neural network, which will be the fundamental building block for a neuromorphic computing architecture. However, on-chip implementation of a large-scale artificial neural network is still very challenging due to unreliable analog weight modulation in current synaptic device technology.

Flexible carbon nanotube Schottky diode and its integrated circuit applications.

Carbon nanotubes (CNTs), a low-dimensional material currently popular in industry and academia, are promising candidates for addressing the limits of existing semiconductors. In particular, CNTs are attractive candidates for flexible electronic materials due to their excellent flexibility and potential applications. In this work, we demonstrate a flexible CNT Schottky diode based on highly purified, preseparated, solution-processed 99% semiconducting CNTs and an integrated circuit application using the CNT Schottky diodes.

Synaptic Device Network Architecture with Feature Extraction for Unsupervised Image Classification.

For the efficient recognition and classification of numerous images, neuroinspired deep learning algorithms have demonstrated their substantial performance. Nevertheless, current deep learning algorithms that are performed on von Neumann machines face significant limitations due to their inherent inefficient energy consumption. Thus, alternative approaches (i.

Three-Dimensional Printed Poly(vinyl alcohol) Substrate with Controlled On-Demand Degradation for Transient Electronics.

Electronics that degrade after stable operation for a desired operating time, called transient electronics, are of great interest in many fields, including biomedical implants, secure memory devices, and environmental sensors. Thus, the development of transient materials is critical for the advancement of transient electronics and their applications. However, previous reports have mostly relied on achieving transience in aqueous solutions, where the transience time is largely predetermined based on the materials initially selected at the beginning of the fabrication.

Three-Dimensionally Printed Micro-electromechanical Switches.

Three-dimensional (3D) printers have attracted considerable attention from both industry and academia and especially in recent years because of their ability to overcome the limitations of two-dimensional (2D) processes and to enable large-scale facile integration techniques. With 3D printing technologies, complex structures can be created using only a computer-aided design file as a reference; consequently, complex shapes can be manufactured in a single step with little dependence on manufacturer technologies. In this work, we provide a first demonstration of the facile and time-saving 3D printing of two-terminal micro-electromechanical (MEM) switches.

Excited state energy fluctuations in the Fenna-Matthews-Olson complex from molecular dynamics simulations with interpolated chromophore potentials.

We analyze the environment-induced fluctuation of pigment excitation energies in the Fenna-Matthews-Olson (FMO) complex from various perspectives, by employing an interpolation-based all-atom potential energy model for describing realistic pigment vibrations. We conduct molecular dynamics simulations on a 100 ns timescale, which is an extent that can enclose the effect of static disorder, and demonstrate its timescale separation from fast dynamic disorder. We extract the spectral densities of the complex by considering both the site and the exciton bases.

Determination of individual contact interfaces in carbon nanotube network-based transistors.

Carbon nanotubes (CNTs) used as semiconducting channels induce high mobility, thermal conductivity, mechanical flexibility, and chemical stability in field-effect, thin-film transistors (TFTs). However, the contact interfaces in CNT-TFTs have contact resistances that are difficult to reduce; this contact resistance can eventually limit the overall performance of CNT-TFTs. The contact interface between the source/drain electrodes and CNTs, especially for those CNT-TFTs in which the channel comprises randomly networked CNTs, plays a particularly dominant role in determining the performance and degree of variability in CNT-TFTs.

Transparent, Flexible Strain Sensor Based on a Solution-Processed Carbon Nanotube Network.

The demands for transparent, flexible electronic devices are continuously increasing due to their potential applications to the human body. In particular, skin-like, transparent, flexible strain sensors have been developed to realize multifunctional human-machine interfaces. Here, we report a sandwich-like structured strain sensor with excellent optical transparency based on highly purified, solution-processed, 99% metallic CNT-polydimethylsiloxane (PDMS) composite thin films.

Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol.

Recent electronic applications require an efficient computing system that can perform data processing with limited energy consumption. Inspired by the massive parallelism of the human brain, a neuromorphic system (hardware neural network) may provide an efficient computing unit to perform such tasks as classification and recognition. However, the implementation of synaptic devices (i.

A Highly Responsive Silicon Nanowire/Amplifier MOSFET Hybrid Biosensor.

This study demonstrates a hybrid biosensor comprised of a silicon nanowire (SiNW) integrated with an amplifier MOSFET to improve the current response of field-effect-transistor (FET)-based biosensors. The hybrid biosensor is fabricated using conventional CMOS technology, which has the potential advantage of high density and low noise performance. The biosensor shows a current response of 5.

Normal versus anomalous self-diffusion in two-dimensional fluids: memory function approach and generalized asymptotic Einstein relation.

Based on the generalized Langevin equation for the momentum of a Brownian particle a generalized asymptotic Einstein relation is derived. It agrees with the well-known Einstein relation in the case of normal diffusion but continues to hold for sub- and super-diffusive spreading of the Brownian particle's mean square displacement. The generalized asymptotic Einstein relation is used to analyze data obtained from molecular dynamics simulations of a two-dimensional soft disk fluid.