Strain engineering is a promising method to manipulate the electronic and optical properties of two-dimensional (2D) materials. However, with weak van der Waals interaction, severe slippage between 2D material and substrate could dominate the bending or stretching processes, leading to inefficiency strain transfer. To overcome this limitation, we report a simple strain engineering method by encapsulating the monolayer 2D material in the flexible PVA substrate through spin-coating approach. The strong interaction force between spin-coated PVA and 2D material ensures the mechanical strain can be effectively transferred with negligible slippage or decoupling. By applying uniaxial strain to monolayer MoS 2 , we observe a higher bandgap modulation up to~300 meV and a highest modulation rate of~136 meV/%, which is approximate two times improvement compared to previous results achieved. Moreover, this simple strategy could be well extended to other 2D materials such as WS 2 or WSe 2 , leading to enhanced bandgap modulation.

Two-dimensional (2D) semiconductors have attracted considerable attention for the development of ultra-thin body transistors. However, the polarity control of 2D transistors and the achievement of complementary logic functions remain critical challenges. Here, we report a doping-free strategy to modulate the polarity of WSe 2 transistors using same contact metal but different integration methods. By applying low-energy van der Waals integration of Au electrodes, we observed robust and optimized p-type transistor behavior, which is in great contrast to the transistors fabricated on the same WSe 2 flake using conventional deposited Au contacts with pronounced n-type characteristics. With the ability to switch majority carrier type and to achieve optimized contact for both electrons and holes, a doping-free logic inverter is demonstrated with higher voltage gain of 340, at the bias voltage of 5.5 V. Furthermore, the simple polarity control strategy is extended for realizing more complex logic functions such as NAND and NOR.

No abstract

Realization of biological synapses using electronic devices is regarded as the basic building blocks for neuromorphic engineering and artificial neural network. With the advantages of biocompatibility, low cost, flexibility, and compatible with printing and roll-to-roll processes, the artificial synapse based on organic transistor is of great interest. In this paper, the artificial synapse simulation by ion-gel gated organic field-effect transistors (FETs) with poly(3-hexylthiophene) (P3HT) active channel is demonstrated. Key features of the synaptic behaviors, such as paired-pulse facilitation (PPF), short-term plasticity (STP), self-tuning, the spike logic operation, spatiotemporal dentritic integration, and modulation are successfully mimicked. Furthermore, the interface doping processes of electrolyte ions between the active P3HT layer and ion gels is comprehensively studied for confirming the operating processes underlying the conductivity and excitatory postsynaptic current (EPSC) variations in the organic synaptic devices. This study represents an important step toward building future artificial neuromorphic systems with newly emerged ion gel gated organic synaptic devices.

Because of the fast expansion of artificial intelligence, development and applications of neuromorphic systems attract extensive interest. In this paper, a highly interconnected neuromorphic architecture (HINA) based on flexible self-supported multiterminal organic transistors is proposed. Au electrodes, poly(3-hexylthiophene) active channels, and ion-conducting membranes were combined to fabricate organic neuromorphic devices. Especially, freestanding ion-conducting membranes were used as gate dielectrics as well as support substrates. Basic neuromorphic behavior and four forms of spike-timing-dependent plasticity were emulated. The fabricated neuromorphic device showed excellent electrical stability and mechanical flexibility after 1000 bends. Most importantly, the device structure is interconnected in a way similar to the neural architecture of the human brain and realizes not only the structure of the multigate but also characteristics of the global gate. Dynamic processes of memorizing and forgetting were incorporated into the global gate matrix simulation. Pavlov's learning rule was also simulated by taking advantage of the multigate array. Realization of HINAs would open a new path for flexible and sophisticated neural networks.