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.
Authors
- Aparajita Mandal (ORCID: https://orcid.org/0000-0001-9070-0286)
- Safiul Alam Mollick (ORCID: https://orcid.org/0000-0002-0985-8914)
- Mukesh Ranjan (ORCID: https://orcid.org/0000-0002-5145-4673)
- T. Som (ORCID: https://orcid.org/0000-0003-1587-6745)
- Dilruba Hasina (ORCID: https://orcid.org/0000-0002-8558-8266)
- Rupam Mandal (ORCID: https://orcid.org/0000-0002-1179-143X)
- Sudheer
Institutions
- 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)
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