Document Type

Conference Proceeding

Publication Date

2017

Abstract

Resistive memory devices have been studied and fabricated using a wide variety of materials including chalcogenides [1], metal oxides [2], and hydrogenated amorphous silicon (a-Si:H) [3]. The most promising materials seem to be amorphous in nature, with the properties of the atomic lattices being conducive to the physical mechanisms that underlie the subsequent resistive switching. The devices are also finding applications beyond high-density digital memory, such as for electronic synapses in neuromorphic systems [4], [5]. However, a different set of properties is required in the latter case compared to devices that must only store binary values. In addition, it is well known that biological synapses are extremely unreliable and noisy, and yet the brain is still able to perform high-level cognitive functions [6]-[8]. This work uses pulse-based electrical characterization techniques to demonstrate the stochastic nature of resistive switching in nanocrystalline silicon (nc-Si) Conductive Bridge Resistive Memory (CBRAM) Devices. We chose nc-Si active layers so these devices could potentially be co-fabricated in the same process as nc-Si TFTs [9]-[11]. Our subsequent findings indicate the device properties may be particularly useful for some non-von Neumann computing paradigms. Though much research has been done using a-Si:H, results from nc-Si CBRAM devices have not been published. In this study, we showed that the switching of the device depends on the history of current passing though it, and not only the voltage applied. Further, the resistance switching in the devices is stochastic, making them ideal candidates for a biologically realistic synapse.

Copyright Statement

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. doi: 10.1109/WMED.2017.7916927

"© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. doi: [DOI #]"

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