Improving Charge Injection at Gold/Conjugated Polymer Contacts by Polymer Insulator-Assisted Annealing for Transistors.

The poor chemical miscibility between metal and organic materials usually leads to both structural and energetic mismatches at gold/organic interfaces, and thereby, high contact resistance of organic electronic devices. This study shows that the contact resistance of organic field-effect transistors is significantly reduced by one order of magnitude, by reforming the contact interface between gold electrodes and conjugated polymers upon a polymer insulator-assisted thermal annealing. Upon an optimized solution process, the conjugated polymer is homogenously distributed within the amorphous polymer insulator matrix with relatively low glass transition temperature, and thus, even a moderate annealing temperature can induce sufficient motion of conjugated polymer chains to simultaneously adjust the polymer orientation and improve the packing of gold atoms. Consequently, gold/conjugated polymer contact is reorganized after annealing, which improves both charge transport from bulk gold to interface and charge injection from gold into conjugated polymers. This method, with appropriate insulator matrix, is effective for improving the injection of both holes and electrons, and widely applicable for many unipolar and ambipolar conjugated polymers to optimize the device performance and simultaneously increase the optical transparency (over 80%). A frequency doubler and a phase modulator are demonstrated, respectively, using the ambipolar transistors with optimized charge injection properties.

Bio-inspired artificial synaptic transistors: evolution from innovative basic units to system integration.

The investigation of transistor-based artificial synapses in bioinspired information processing is undergoing booming exploration, and is the stable building block for brain-like computing. Given that the storage and computing separation architecture of von Neumann construction is not conducive to the current explosive information processing, it is critical to accelerate the connection between hardware systems and software simulations of intelligent synapses. So far, various works based on a transistor-based synaptic system successfully simulated functions similar to biological nerves in the human brain. However, the influence of the semiconductor and the device structural design on synaptic properties is still poorly linked. This review concretely emphasizes the recent advances in the novel structure design of semiconductor materials and devices used in synaptic transistors, not only from a single multifunction synaptic device but also to system application with various connected routes and related working mechanisms. Finally, crises and opportunities in transistor-based synaptic interconnection are discussed and predicted.

High-performance asymmetric electrode structured light-stimulated synaptic transistor for artificial neural networks.

Photonics neuromorphic computing shows great prospects due to the advantages of low latency, low power consumption and high bandwidth. Transistors with asymmetric electrode structures are receiving increasing attention due to their low power consumption, high optical response, and simple preparation technology. However, intelligent optical synapses constructed by asymmetric electrodes are still lacking systematic research and mechanism analysis. Herein, we present an asymmetric electrode structure of the light-stimulated synaptic transistor (As-LSST) with a bulk heterojunction as the semiconductor layer. The As-LSST exhibits superior electrical properties, photosensitivity and multiple biological synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, and long-term memory. Benefitting from the asymmetric electrode configuration, the devices can operate under a very low drain voltage of 1 × 10-7 V, and achieve an ultra-low energy consumption of 2.14 × 10-18 J per light stimulus event. Subsequently, As-LSST implemented the optical logic function and associative learning. Utilizing As-LSST, an artificial neural network (ANN) with ultra-high recognition rate (over 97.5%) of handwritten numbers was constructed. This work presents an easily-accessible concept for future neuromorphic computing and intelligent electronic devices.

Artificial Tactile Recognition Enabled by Flexible Low-Voltage Organic Transistors and Low-Power Synaptic Electronics.

The advancement of self-powered intelligent strain systems for human-computer interaction is crucial toward wearable and energy-saving applications. Simultaneously, lowering operating voltage and thus reducing power consumption are of particular interests. A brain-like smart synaptic hardware system is considered as a promising candidate for low-power, parallel computing and learning processes. However, the combination of low-voltage organic transistors and energy efficient smart synapse hardware systems driven by a tactile signal has been hindered by the limited materials and technology. Here, by employing an elastomeric copolymer poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) with a high HFP content of 25 mol %, flexible, low-voltage transistors (|VG| ≤ 3 V) and a low energy consumption synapse ≤ 9.2 × 10-17 J are devised simultaneously, along with the lowest quality factor (R = Pw × VG, 2.76 × 10-16 J V). Furthermore, based on the low voltage and low power consumption characteristics, flexible artificial tactile recognition system and Morse code recognition are established without any computing supporting. Mechanical flexibility, cycling stability, image contrast enhancement functions, and simulated pattern recognition accuracy of the multilayer perceptron neural network are also simulated. This work recommends a route of exploiting low voltage, low power consumption synaptic systems and smart human-machine interfaces with low energy loss based on flexible organic synaptic transistors.