journal article·2021-03-23Open access

Nanoporous Dielectric Resistive Memories Using Sequential Infiltration Synthesis

Bhaswar Chakrabarti, Henry Chan, Khan Alam, Aditya Koneru, Thomas E. Gage, Leonidas E. Ocola, Ralu Divan, Daniel Rosenmann, Abhishek Khanna, Benjamin Grisafe, Toby Sanders, Suman Datta, Ilke Arslan, Subramanian K. R. S. Sankaranarayan, Supratik Guha

ACS Nano· Volume 15· Issue 3

Abstract

Resistance switching in metal–insulator–metal structures has been extensively studied in recent years for use as synaptic elements for neuromorphic computing and as nonvolatile memory elements. However, high switching power requirements, device variabilities, and considerable trade-offs between low operating voltages, high on/off ratios, and low leakage have limited their utility. In this work, we have addressed these issues by demonstrating the use of ultraporous dielectrics as a pathway for high-performance resistive memory devices. Using a modified atomic layer deposition based technique known as sequential infiltration synthesis, which was developed originally for improving polymer properties such as enhanced etch resistance of electron-beam resists and for the creation of films for filtration and oleophilic applications, we are able to create ∼15 nm thick ultraporous (pore size ∼5 nm) oxide dielectrics with up to 73% porosity as the medium for filament formation. We show, using the Ag/Al2O3 system, that the ultraporous films result in ultrahigh on/off ratio (>109) at ultralow switching voltages (∼±600 mV) that are 10× smaller than those for the bulk case. In addition, the devices demonstrate fast switching, pulsed endurance up to 1 million cycles. and high temperature (125 °C) retention up to 104 s, making this approach highly promising for large-scale neuromorphic and memory applications. Additionally, this synthesis methodology provides a compatible, inexpensive route that is scalable and compatible with existing semiconductor nanofabrication methods and materials.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Semiconductor materials and devices · Ferroelectric and Negative Capacitance Devices

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

Affiliations

Institute for Molecular Engineering, Eckhardt Research Center, University of Chicago, 5640 S. Ellis Avenue, Chicago, Illinois 60637, United States

Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States

Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States

Department of Electrical Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States

School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona 85287, United States

Funding

Division of Computing and Communication Foundations · Office of Naval Research · Semiconductor Research Corporation · Nanoelectronics Research Corporation

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