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The critical role of materials and device geometry on performance of RRAM and memristor: Review

Mohammad Tauquir Alam Shamim Shaikh, Chowdam Venkata Prasad, Kyong Jae Kim, You Seung Rim

2025Materials Today Physics14 citationsDOIOpen Access PDF

Abstract

In the rapidly evolving field of memory technology, material strategies have been continuously optimized to achieve high-performance memory devices, many of which have successfully transitioned to industrial applications. A critical focus has been placed on selecting and refining materials that are environmentally sustainable and amenable to facile processing methods. While resistive random-access memory (RRAM) materials, mechanisms, and applications have been comprehensively reviewed, studies focusing on strategic approaches to material optimization remain limited. This review delves into the burgeoning domain of polymer/organic memory and memristors , with particular attention to electrode and switching layer (SL) material modifications. Key strategies include blending polymers, incorporating nanoparticles , quantum dots , or nanosheets into the SL, and fabricating bilayer or multilayer SLs within the metal-insulator-metal (MIM) structure. These materials and their configurations play pivotal roles in enabling various memory types (WORM, NVM, VM) and achieving low-voltage operation, critical for reducing energy consumption and improving device longevity. By interlinking phenomena and presenting unique features from literature, this review offers readers insights into innovative approaches to materials selection, device geometry, and modulation of biasing stimuli. It serves as a comprehensive guide towards understanding of materials strategies in organic RRAM devices for next-generation memory and memristor technologies.

Topics & Concepts

Materials scienceMemristorResistive random-access memoryEngineering physicsNanotechnologyOptoelectronicsElectrical engineeringEngineeringVoltageAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeuroscience and Neural Engineering