The explosive increase in digital communications in the Big Data and internet of Things era spurs the development of universal memory that can run at high speed with high-density and nonvolatile storage capabilities, as well as demonstrating superior mechanical flexibility for wearable applications. Among various candidates for the next-generation information storage technology, resistive switching memories distinguish themselves with low power consumption, excellent downscaling potential, easy 3D stacking, and high CMOS compatibility, fulfilling key requirements for high-performance data storage. Employing organic and hybrid switching media in addition allows light weight and flexible integration of molecules with tunable device performance via molecular design-cum-synthesis strategy. In this review, we present a timely and comprehensive review of the recent advances in organic and hybrid resistive switching materials and devices, with particular attention on their design principles for electronic property tuning and flexible device performance. The current challenges posed with development of organic and hybrid resistive switching materials and flexible memory devices, together with their future perspectives, are also discussed.

Polymer materials have been considered as promising candidates for the implementation of memristor devices due to their low-cost, easy solution processability, mechanical flexibility and ductibility, tunable electronic performance through innovative molecular design cum synthesis strategy and compatibility with complementary metal oxide semiconductor (CMOS) technology as well. The digital-type polymer memristor behaves as resistive random access memory with non-volatility, high density, more speed, low power consumption, large ON/OFF ratio, high endurance and long retention, and is recognized as an appealing candidate for the next generation “universal memory”. As a logic component, the analog-type memristor, with the ability to emulate the fundamental synaptic functions of short-term/long-term plasticity (STP/LTP), spike-timing dependent-plasticity (STDP), spike-rate dependent plasticity (SRDP) and “learning-experience” behaviors, can be used to construct artificial neural networks for neuromorphic computation. In this review, we shall attempt to summarize the recent progress in research on the materials, switching characteristics and mechanism aspects of two terminal polymer memristors, for both information storage and neuromorphic applications that inspire great interest in the industrial and academic communities.

Polymer memristors with light weight and mechanical flexibility are preeminent candidates for low-power edge computing paradigms. However, the structural inhomogeneity of most polymers usually leads to random resistive switching characteristics, which lowers the production yield and reliability of nanoscale devices. In this contribution, we report that by adopting the two-dimensional conjugation strategy, a record high 90% production yield of polymer memristors has been achieved with miniaturization and low power potentials. By constructing coplanar macromolecules with 2D conjugated thiophene derivatives to enhance the π–π stacking and crystallinity of the thin film, homogeneous switching takes place across the entire polymer layer, with fast responses in 32 ns, D2D variation down to 3.16% ~ 8.29%, production yield approaching 90%, and scalability into 100 nm scale with tiny power consumption of ~ 10⁻¹⁵ J/bit. The polymer memristor array is capable of acting as both the arithmetic-logic element and multiply-accumulate accelerator for neuromorphic computing tasks.