journal article·2020-09-18Open access

Carbon resistive probe memory designed for ultra-high storage density

Zhi-Cheng Liu, Lei Wang

Nanotechnology· Volume 31· Issue 38

Abstract

Abstract Probe-based storage memories are considered one of the most promising solutions to address the mass storage issues in the near future. However, data size arising from conventional probe memories is usually larger than probe size due to the thermal diffusion effect. To eliminate such thermal interference and make data dimension fully dominated by probe dimension, we proposed a concept of carbon-based resistive probe memory and developed a comprehensive computational model to predict its write, rewrite and readout performances governed by electro-thermal and mass concentration processes. The physical reality of such a theoretical model was demonstrated through the good agreement between the calculated and experimental measured threshold voltages for different layered thickness. The data bit of carbon-based resistive probe memory, considered as the sp 2 filament inside sp 3 background, is formed completely underneath the tip edge due to the localized electric field induced here. This makes the bit size fully determined by the probe tip dimension and allows for the achievement of ultra-high density using an ultra-small probe tip with low energy consumption. Such a conductive filament can be also rewritten back to its pristine sp 3 state at relatively high temperature (~250 °C) and detected by sensing the device reading contrast (~1). The designed carbon-based resistive probe memory can retain its bit completeness even if we reduce the bit pitch to 28 nm for a probe size of 25 nm, exhibiting a superior immunity to thermal cross-talk effect. It, however, induces strong readout cross-talk, which is revealed from the resistance image of the multiple bit pattern. This adversely reduces the achievable recording density due to the required large bit pitch, which can be alleviated using either a very sharp tip apex or the optical readout scheme.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Semiconductor materials and devices · Electronic and Structural Properties of Oxides

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

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