Abstract
This work reports 2-bits/cell hafnium oxide-based stacked resistive random access memory devices fabricated on flexible polyimide substrates for neuromorphic applications considering the high thermal budget. The ratio of low-resistance state current (ION) to high-resistance state current (IOFF) or ION/IOFF for the fabricated devices was above 1.4×103 with a low device-to-device variation at 100 μA current compliance. The mechanical stability over 104 bending cycles at a 5 mm bending radius and endurance over 106 WRITE cycles makes these devices suitable for online neural network training. The data retention capability over 104s at 125°C also infuses these devices' long-term inference capability. Furthermore, the performance of the devices has been verified for neuromorphic applications by system-level simulations with experimentally calibrated data. The system-level simulation reveals only a 2% loss in inference accuracy over ten years from the baseline.
Original language | English |
---|---|
Pages (from-to) | 4737-4743 |
Number of pages | 7 |
Journal | IEEE Transactions on Electron Devices |
Volume | 69 |
Issue number | 8 |
DOIs | |
Publication status | Published - 2022 Aug 1 |
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering