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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.

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.

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