학술논문

Thermal Induced Retention Degradation of RRAM-based Neuromorphic Computing Chips
Document Type
Conference
Source
2023 IEEE International Reliability Physics Symposium (IRPS) Reliability Physics Symposium (IRPS), 2023 IEEE International. :1-6 Mar, 2023
Subject
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Temperature measurement
Semiconductor device measurement
Temperature
Neuromorphic engineering
Computational modeling
Neural networks
Reliability
RRAM-based chip
thermal
reliability
retention
neuromorphic computing
Language
ISSN
1938-1891
Abstract
The retention characteristic of RRAM devices will obviously decrease the computing reliability of RRAM-based neuromorphic computing chips. In this paper, with the help of thermal simulation, we evaluated the thermal effect on the retention characteristic of RRAM devices and the computing accuracy of RRAM-based neuromorphic computing chips. We find weight dual allocation can achieve $\boldsymbol{ < 1\%}$ accuracy loss in ten years, as compared to 1% accuracy loss in just 4.6 days when one 1T1R unit is used to express one weight. The results also show that the lower temperature can also help to improve the computing reliability of neuromorphic computing chips.