ReviewMicromachines2026
RRAM-Based Neuromorphic Devices for Artificial-Intelligence Hardware: Device Physics, Materials, Processing, and Packaging.
Review in Micromachines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Resistive random-access memory (RRAM) has emerged as one of the most promising device platforms for neuromorphic, in-memory computing because its two-terminal metal-insulator-metal (MIM) structure can reproduce the weight-update behavior of biological synapses while remaining compatible with mainstream CMOS processing. This review summarizes the current state of RRAM-based neuromorphic technology from four complementary perspectives: device physics, materials, fabrication processes, and packaging. We first describe the operating principles of filamentary and interface-type RRAM, including the forming/set/reset switching sequence, and the two dominant analytical frameworks used to describe the reset transition-the ion-migration model and the thermally driven filament-dissolution model. We then review the switching-layer and electrode materials that have been most widely investigated such as HfO
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