90% yield production of polymer nano-memristor for in-memory computing

Abstract

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.

Donor-acceptor-type poly[chalcogenoviologen-alt-triphenylamine] for synaptic biomimicking and neuromorphic computing

“…The human memory mainly benefits from the natural evolution of neural networks that exhibit several outstanding properties such as massive parallel processing, in-memory computing architecture, event-driven operation and others. Since the discovery of the first real memristor at HP Labs in 2008 ( Strukov et al., 2008 ), great efforts have been devoted to developing novel memristive functional materials and devices to construct artificial neural networks for neuromorphic computation and emulate the physiological functions (e.g., the learning, memorizing, forgetting, decision-making, and judging actions) of biological synapses ( Chen et al., 2014 ; Zhang et al.,2018 , Zhang et al, 2019 , 2020 , Zhang et al, 2021 ; Van De Burgt et al., 2018 ; Choi et al.,2018 , 2020 ; Liu et al.,2016 , 2018 ; Li et al., 2017 ; Wan et al., 2020 ; Kim et al., 2018 ; Wang et al., 2014 , 2015 ; Ren et al., 2020 ; McFarlane et al., 2020 ). Recently, it was found that the negative photoconductance effect observed impressively in the high resistance state branch of the resistive switching memory enabled the memristor function to be extended to both memory logic display and multistate data storage ( Zhou et al., 2021 ).…”

“…Since 2005, polymer memories have been proposed to revolutionize electrical applications by providing extremely inexpensive, lightweight, and transparent modules that can be fabricated onto plastic, glass, or the top layer of the complementary metal-oxide semiconductor circuits. Similar to these inorganic materials, some polymer functional materials, including metal-containing polymers, polymer-based multi-component redox systems, and pure polymers, have also been found to exhibit excellent memristive performance in recent years ( Chen et al., 2014 ; Zhang et al.,2018 , Zhang et al, 2019 , 2020 , Zhang et al, 2021 ; Wang et al.,2014 , 2015 ; Liu et al., 2016 ; Ren et al., 2020 ; McFarlane et al., 2020 ; Pincella et al., 2011 ; Bandyopadhyay et al., 2011 ). With these polymer memristors, one can observe nonlinear transmission characteristics similar to that of a biological synapse.…”

“…By using polyfluorene functionalized with triphenylamine and ferrocene moieties side chains as an active layer, the as-fabricated electronic device, which shows excellent memristive switching and multilevel memory performance, is capable of executing multilevel memory; decimal arithmetic operations of addition, subtraction, multiplication, and division; as well as simple Boolean logic operations. Recently, we designed and synthesized a two-dimensional conjugated polymer PBDTT-BQTPA ( Zhang et al, 2021 ). This material-based electronic device exhibited a rapid transition from the OFF state to the ON state within ∼32 ns.…”

Recent advances on crystalline materials-based flexible memristors for data storage and neuromorphic applications

“…Recently, memristors have emerged as promising contenders for next-generation high-capacity information storage and computing systems, attributing to their advantages of fast data transfer rate, short access time, low power consumption, and the compatibility with complementary metal-oxide-semiconductor (CMOS) technology [8][9][10][11][12][13][14]. More importantly, they have exhibited great potential in the applications of nonvolatile memory, logic computing and brain-inspired neuromorphic hardware [15][16][17][18][19][20][21][22].…”

“…More importantly, they have exhibited great potential in the applications of nonvolatile memory, logic computing and brain-inspired neuromorphic hardware [15][16][17][18][19][20][21][22]. These three interrelated technologies provide a feasible route for developing a novel in-memory computing architecture that integrates information storage and processing in one system [12,13], which can break through the existing von Neumann bottleneck and memory wall of traditional computing systems. A typical memristor device generally composes of two electrodes and a switching layer between them, which can switch between high and low resistance states (RSs) in response to an external electric voltage.…”