Medium-Temperature-Oxidized GeOx Resistive-Switching Random-Access Memory and Its Applicability in Processing-in-Memory Computingopen access
- Authors
- Mohanan, Kannan Udaya; Cho, Seongjae; Park, Byung-Gook
- Issue Date
- Jul-2022
- Publisher
- SPRINGER
- Keywords
- Medium-temperature oxidation; Germanium oxide; Resistive-switching random-access memory (ReRAM); Low-power hardware neural network; Processing-in-memory (PIM)
- Citation
- NANOSCALE RESEARCH LETTERS, v.17, no.1
- Journal Title
- NANOSCALE RESEARCH LETTERS
- Volume
- 17
- Number
- 1
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84945
- DOI
- 10.1186/s11671-022-03701-8
- ISSN
- 1931-7573
- Abstract
- Processing-in-memory (PIM) is emerging as a new computing paradigm to replace the existing von Neumann computer architecture for data-intensive processing. For the higher end-user mobility, low-power operation capability is more increasingly required and components need to be renovated to make a way out of the conventional software-driven artificial intelligence. In this work, we investigate the hardware performances of PIM architecture that can be presumably constructed by resistive-switching random-access memory (ReRAM) synapse fabricated with a relatively larger thermal budget in the full Si processing compatibility. By introducing a medium-temperature oxidation in which the sputtered Ge atoms are oxidized at a relatively higher temperature compared with the ReRAM devices fabricated by physical vapor deposition at room temperature, higher device reliability has been acquired. Based on the empirically obtained device parameters, a PIM architecture has been conceived and a system-level evaluations have been performed in this work. Considerations include the cycle-to-cycle variation in the GeOx ReRAM synapse, analog-to-digital converter resolution, synaptic array size, and interconnect latency for the system-level evaluation with the Canadian Institute for Advance Research-10 dataset. A fully Si processing-compatible and robust ReRAM synapse and its applicability for PIM are demonstrated.
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