Grainy Cr <sub> <i>x</i> </sub> O <sub> <i>y</i> </sub> RRAM with Thermal Stability for Enabling Emerging Computing Applications
With the increasing demand for high-performance data processing, traditional silicon-based integrated circuits and emerging memory technologies are in urgent need as device miniaturization approached physical scaling limits. Resistive random-access memory (RRAM) is a promising candidate for neuromorphic computing and nonvolatile memory applications for its high scalability and low cost. With the rapid growth of AI workloads, ensuring device-level thermal robustness in non-volatile memory technologies has become increasingly critical. In this work, CrxOy-based RRAM devices were fabricated by localized prepatterned metal sources by chemical vapor deposition at 650 °C, as redistributed localized oxide deposition. The grainy oxide RRAM devices exhibit reliable bipolar switching with high uniformity and an ON/OFF ratio exceeding 104, while operated under 25 to 70 °C with an increased memory window attributed to the grainy structures with a thermal treatment process, which stabilized the filamentary-based resistive switching under high temperature. Linear conductance modulation with long-term potentiation (LTP), long-term depression (LTD), and training accuracy on the MNIST data set has resulted in 92.4% of pattern recognition accuracy. This study provides a unique technique in fabricating the active switching layer as CrxOy with improved operating temperature robustness, good memory window, and analog behaviors for future in-memory computing applications.
Authors
- Ying‐Chen Chen (ORCID: https://orcid.org/0000-0002-9313-968X)
- Zhipeng Lu (ORCID: https://orcid.org/0000-0002-9992-6713)
- Sandhya Susarla (ORCID: https://orcid.org/0000-0003-1773-0993)
- Mahir Manna
- Yujian Huang
- Sai Prakash Maddineni
- David J. Niedzwieck
Institutions
- Newton Public Schools (US)
- Energy Concepts (United States) (US)
- Good Shepherd Rehabilitation (US)
- Arizona State University (US)
Publication Details
- Journal
- ACS Applied Electronic Materials
- Published
- 2025-12-12
- DOI
- https://doi.org/10.1021/acsaelm.5c02188
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.46