Chargeable polymer electret‐based organic field‐effect transistor (OFET) memory devices are attractive for intrinsically flexible artificial synapse. However, the effects of molecular weight (Mw) of polymer electret on the charge trapping properties, especially the implementation of synaptic functions, are not yet well‐understood. In this work, the authors study pentacene‐based synaptic OFET memory made from two different molecular weight polymer electrets, with poly(N‐vinylcarbazole) (PVK) as the case study. Utilizing the synergistic effect of smooth surface morphology, higher polymer chain‐end density and lower dielectric constant, OFET memory device with lower Mw PVK electret showed a larger memory window (28.2 V) and faster write speed (1 ms) compared to that with higher Mw PVK. Benefiting from its charge‐trapping ability, the synaptic OFETs with lower Mw PVK delivered a better modulation of synaptic weight. The co‐modulation of photonic and electric operations enabled the reconfigurable short‐term plasticity and long‐term plasticity (LTP) in lower Mw PVK devices, further leading to the STP‐based emulation of visible color recognition and LTP‐based neural network simulation. This work enlightens a detailed understanding of the molecular weight‐dependent charge trapping behavior for the future integration of visible information sensing‐memory‐processing in optoelectronic OFET memory.
Reconfigurable phototransistor memory attracts considerable attention for adaptive visuomorphic computing, with highly efficient sensing, memory, and processing functions integrated onto a single device. However, developing reconfigurable phototransistor memory remains a challenge due to the lack of an all‐optically controlled transition between short‐term plasticity (STP) and long‐term plasticity (LTP). Herein, an air‐stable Zr‐CsPbI3 perovskite nanocrystal (PNC)‐based phototransistor memory is designed, which is capable of broadband photoresponses. Benefitting from the different electron capture ability of Zr‐CsPbI3 PNCs to 650 and 405 nm light, an artificial synapse and non‐volatile memory can be created on‐demand and quickly reconfigured within a single device for specific purposes. Owing to the optically reconfigurable and wavelength‐aware operation between STP and LTP modes, the integrated blue feature extraction and target recognition can be demonstrated in a homogeneous neuromorphic vision sensor array. This work suggests a new way in developing perovskite optoelectronic transistors for highly efficient in‐sensor computing.
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.
Synaptic Transistors In article 2208497, Haifeng Ling, Linghai Xie, Wei Huang, and co‐workers propose a wavelength‐aware reconfigurable synaptic transistor for visuomorphic computing. Based on the broadband absorption of Zr‐CsPbI3 perovskite nanocrystals, reconfigurability between short‐term plasticity and long‐term plasticity for the device is realized. A vision sensor array is constructed to simulate image pre‐processing and recognition with a homogeneous architecture.
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.
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