journal article·2020-06

Modulated filamentary conduction of Ag/TiO2 core-shell nanowires to impart extremely sustained resistance switching behavior in a flexible composite

Youngjin Kim, Woojin Jeon, Minsung Kim, Jong Hyuk Park, Cheol Seong Hwang, Sang-Soo Lee

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Abstract

Abstract A core-shell nanowire-embedded composite formulation, exhibiting exquisite resistance switching behavior at quite low operating voltage (∼0.1 V) and extremely sustained switching behavior even under repeated mechanical deformation, has been developed for the application of a non-volatile memory device. This structure is based on a simple spin-casted composite comprising a stochastic distribution of Ag/TiO2 core-shell nanowires deploying resistance switching behavior, and poly(vinyl alcohol) (PVA) as a dielectric matrix. The Ag/TiO2 core-shell architecture of the one-dimensional (1D) resistance switching fillers (RSFs) and their distribution on a two-dimensional electrode surface were both designed to confine the electric field to the contact points between the RSFs and electrodes, imparting facile filamentary conduction in a highly confined region. These structural features render the switching behavior highly sustainable against mechanical stress and electrical noise; consequently, a notable switching operation, including a quite narrow variation of switching parameters, and very low operating voltages (Vset ∼ 0.098 ± 0.011 V, and Vreset ∼ −0.102 ± 0.013 V) were all successfully obtained for up to ∼50,000 mechanical deformation cycles. The resistance switching memory employing the core-shell nanowire-embedded composite formulation is highly promising for applications in which mechanical resilience, transparency, device lifetime, and power conservation are crucial, such as wearable electronics.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Conducting polymers and applications · Advanced Sensor and Energy Harvesting Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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