Pseudo-transistors for emerging neuromorphic electronics.

Artificial synaptic devices are the cornerstone of neuromorphic electronics. The development of new artificial synaptic devices and the simulation of biological synaptic computational functions are important tasks in the field of neuromorphic electronics. Although two-terminal memristors and three-terminal synaptic transistors have exhibited significant capabilities in the artificial synapse, more stable devices and simpler integration are needed in practical applications. Combining the configuration advantages of memristors and transistors, a novel pseudo-transistor is proposed. Here, recent advances in the development of pseudo-transistor-based neuromorphic electronics in recent years are reviewed. The working mechanisms, device structures and materials of three typical pseudo-transistors, including tunneling random access memory (TRAM), memflash and memtransistor, are comprehensively discussed. Finally, the future development and challenges in this field are emphasized.

Dual-Adaptive Heterojunction Synaptic Transistors for Efficient Machine Vision in Harsh Lighting Conditions.

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes.

Low-power flexible organic memristor based on PEDOT:PSS/pentacene heterojunction for artificial synapse.

Organic synaptic memristors are of considerable interest owing to their attractive characteristics and potential applications to flexible neuromorphic electronics. In this work, an organic type-II heterojunction consisting of poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) and pentacene was adopted for low-voltage and flexible memristors. The conjugated polymer PEDOT:PSS serves as the flexible resistive switching (RS) layer, while the thin pentacene layer plays the role of barrier adjustment. This heterojunction enabled the memristor device to be triggered with low-energy RS operations (V < ± 1.0 V and I < 9.0 µA), and simultaneously providing high mechanical bending stability (bending radius of ≈2.5 mm, bending times = 1,000). Various synaptic properties have been successfully mimicked. Moreover, the memristors presented good potentiation/depression stability with a low cycle-to-cycle variation (CCV) of less than 8%. The artificial neural network consisting of this flexible memristor exhibited a high accuracy of 89.0% for the learning with MNIST data sets, even after 1,000 tests of 2.5% stress-strain. This study paves the way for developing low-power and flexible synaptic devices utilizing organic heterojunctions.