Low-Energy Ion-Implanted Nanometer-Thick Metal Oxide Memristor for Random Number Generation at the Nanoscale

Resistive random-access memory (ReRAM) devices have emerged to be a promising source for ample information storage in next-generation nonvolatile memories (NVMs) and true random number generation (TRNG)-based robust cybersecurity. However, cycle-to-cycle performance degradation at the nanoscale results in deviation of the normal probability distribution, limiting ReRAM applicability to micron-size devices. This work demonstrates the efficacy of low-energy Au-ion implantation in nanometer-thick HfOx films in achieving a superior ReRAM performance at the nanoscale toward TRNG application. The role of Au-ion implantation in a single amorphous HfOx layer is demonstrated to create high-performance memory cells at the nanoscale. Local probe microscopy analysis provides a spatial identification of homogeneously distributed reproducible conductive filament locations and its one-to-one correlation with surface topography. The Au-implanted films exhibit a highly stable, forming-free bipolar resistive switching, having well-separated set and reset transitions with significantly small fluctuations (as low as 3.6% and 3.2%, respectively). The films display a negligible cycle-to-cycle degradation in low and high resistive states, thereby resulting in an outstanding cumulative probability distribution. The chi-square tests further reveal that the low resistive state current is an excellent physical source of six distinct random states. Hence, the ion implantation on economically viable and fabrication-friendly HfOx material is found to be a robust technique to fabricate high-performance ReRAM devices for future ultrahigh-integration density NVMs and TRNG sources for cybersecurity applications.

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Publication Details

Journal
ACS Applied Nano Materials
Published
2025-03-26
DOI
https://doi.org/10.1021/acsanm.4c07144
Citations
4
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.92
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article

Low-Energy Ion-Implanted Nanometer-Thick Metal Oxide Memristor for Random Number Generation at the Nanoscale

Aparajita Mandal, Safiul Alam Mollick, Mukesh Ranjan, T. Som et al.
4 citations
ACS Applied Nano Materials
Advanced Memory and Neural Computing
2.92
article

Low-Energy Ion-Implanted Nanometer-Thick Metal Oxide Memristor for Random Number Generation at the Nanoscale

Aparajita Mandal, Safiul Alam Mollick, Mukesh Ranjan, T. Som, Dilruba Hasina, Rupam Mandal, Sudheer
article en
4 citations

Abstract

Resistive random-access memory (ReRAM) devices have emerged to be a promising source for ample information storage in next-generation nonvolatile memories (NVMs) and true random number generation (TRNG)-based robust cybersecurity. However, cycle-to-cycle performance degradation at the nanoscale results in deviation of the normal probability distribution, limiting ReRAM applicability to micron-size devices. This work demonstrates the efficacy of low-energy Au-ion implantation in nanometer-thick HfOx films in achieving a superior ReRAM performance at the nanoscale toward TRNG application. The role of Au-ion implantation in a single amorphous HfOx layer is demonstrated to create high-performance memory cells at the nanoscale. Local probe microscopy analysis provides a spatial identification of homogeneously distributed reproducible conductive filament locations and its one-to-one correlation with surface topography. The Au-implanted films exhibit a highly stable, forming-free bipolar resistive switching, having well-separated set and reset transitions with significantly small fluctuations (as low as 3.6% and 3.2%, respectively). The films display a negligible cycle-to-cycle degradation in low and high resistive states, thereby resulting in an outstanding cumulative probability distribution. The chi-square tests further reveal that the low resistive state current is an excellent physical source of six distinct random states. Hence, the ion implantation on economically viable and fabrication-friendly HfOx material is found to be a robust technique to fabricate high-performance ReRAM devices for future ultrahigh-integration density NVMs and TRNG sources for cybersecurity applications.

ACS Applied Nano MaterialsVol. 8(13)
University of Burdwan (IN), Homi Bhabha National Institute (IN), Institute of Physics, Bhubaneshwar (IN), Institute for Plasma Research (IN), J. S. Ayurveda Mahavidyalaya and P.D. Patel Ayurveda Hospital (IN), Evans Analytical Group (United States) (US)
Affordable and clean energy
Openalex Percentile: Top 10%
Advanced Memory and Neural Computing
2.92
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