Terpolymerization is a feasible approach to optimize the device performance of organic semiconductors. Yet, since most reported terpolymers utilize a one-pot polymerization method, the regularity of the polymer backbone is...

Enhancement of the conductance range of memristors used in synaptic devices is essential for achieving high‐performance neural networks. Herein, a memristor based on the stack structure of TiN/AlO x /AlO y /ITO is designed to enhance the conductance range. The AlO x /AlO y devices exhibit pseudointerface switching characteristics with higher switching ratios and reliability under a compliance current of 1 mA. The high‐resistance state/low‐resistance state ratio of the AlO x /AlO y devices increases from 11.4 to 128.6 compared with the AlO x devices. Accompanied by high‐conductance linearity, the conductance range increases from 36–202 to 25–280 μS simultaneously. Based on the related electrical properties and microstructure analysis, the regulation mechanism of the formation and rupture of conductive filaments by the oxygen concentration gradient is demonstrated. Simulations using the Modified National Institute of Standards and Technology (MNIST) handwritten recognition data set prove that the AlO x /AlO y memristor can operate with a learning accuracy of 91.07%.

In order to meet the exponentially increased demand for data processing, researchers are exploring memristors to emulate synapse or in-memory computing. To further enhance its performance, the impact of oxygen content on storage and synaptic performances are investigated based on Ag/TaxOy/ITO memristors. Conductive filament dominated mechanism with two kinds of ions is validated by multiple methods. By optimizing oxygen content, synaptic weight modulation ability increased almost 7 folds. Additionally, Boolean logic operations are implemented with >10000 switching cycles and in-situ stored more than 10000s. Our work lays the foundation of optimizing memory storage and neuromorphic performances on future in-memory computing.