Reinforcing Synaptic Plasticity of Defect-Tolerant States in Alloyed 2D Artificial Transistors.

While two-dimensional (2D) materials possess the desirable future of neuromorphic computing platforms, unstable charging and de-trapping processes, which are inherited from uncontrollable states, such as the interface trap between nanocrystals and dielectric layers, can deteriorate the synaptic plasticity in field-effect transistors. Here, we report a facile and effective strategy to promote artificial synaptic devices by providing physical doping in 2D transition-metal dichalcogenide nanomaterials. Our experiments demonstrate that the introduction of niobium (Nb) into 2D WSe2 nanomaterials produces charge trap levels in the band gap and retards the decay of the trapped charges, thereby accelerating the artificial synaptic plasticity by encouraging improved short-/long-term plasticity, increased multilevel states, lower power consumption, and better symmetry and asymmetry ratios. Density functional theory calculations also proved that the addition of Nb to 2D WSe2 generates defect tolerance levels, thereby governing the charging and de-trapping mechanisms of the synaptic devices. Physically doped electronic synapses are expected to be a promising strategy for the development of bioinspired artificial electronic devices.

Environmentally Stable and Reconfigurable Ultralow-Power Two-Dimensional Tellurene Synaptic Transistor for Neuromorphic Edge Computing.

While neuromorphic computing can define a new era for next-generation computing architecture, the introduction of an efficient synaptic transistor for neuromorphic edge computing still remains a challenge. Here, we envision an atomically thin 2D Te synaptic device capable of achieving a desirable neuromorphic edge computing design. The hydrothermally grown 2D Te nanosheet synaptic transistor apparently mimicked the biological synaptic nature, exhibiting 100 effective multilevel states, a low power consumption of ∼110 fJ, excellent linearity, and short-/long-term plasticity. Furthermore, the 2D Te synaptic device achieved reconfigurable MNIST recognition accuracy characteristics of 88.2%, even after harmful detergent environment infection. We believe that this work serves as a guide for developing futuristic neuromorphic edge computing.

Scalability of carbon-nanotube-based thin film transistors for flexible electronic devices manufactured using an all roll-to-roll gravure printing system.

To demonstrate that roll-to-roll (R2R) gravure printing is a suitable advanced manufacturing method for flexible thin film transistor (TFT)-based electronic circuits, three different nanomaterial-based inks (silver nanoparticles, BaTiO3 nanoparticles and single-walled carbon nanotubes (SWNTs)) were selected and optimized to enable the realization of fully printed SWNT-based TFTs (SWNT-TFTs) on 150-m-long rolls of 0.25-m-wide poly(ethylene terephthalate) (PET). SWNT-TFTs with 5 different channel lengths, namely, 30, 80, 130, 180, and 230 µm, were fabricated using a printing speed of 8 m/min. These SWNT-TFTs were characterized, and the obtained electrical parameters were related to major mechanical factors such as web tension, registration accuracy, impression roll pressure and printing speed to determine whether these mechanical factors were the sources of the observed device-to-device variations. By utilizing the electrical parameters from the SWNT-TFTs, a Monte Carlo simulation for a 1-bit adder circuit, as a reference, was conducted to demonstrate that functional circuits with reasonable complexity can indeed be manufactured using R2R gravure printing. The simulation results suggest that circuits with complexity, similar to the full adder circuit, can be printed with a 76% circuit yield if threshold voltage (Vth) variations of less than 30% can be maintained.