HfOx-based Resistive Random Access Memory for neuromorphic computing application

• Main Authors: Chu, Chun-An, 朱俊安
• Other Authors: Tseng, Tseung-Yuen
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/ccvue3

碩士 === 國立交通大學 === 電子研究所 === 107 === Due to that the device toward more powerful, Artificial Intelligence (AI) computing become more and more popular. Neuromorphic computing is one of the most popular model of AI computing. By simulating the weight update of synapses between the neuro cells when the human brain accepts outside signal, we can use a new way to update the hardware condition instead of software. It is expected that the AI computing become much faster and much lower power consumption. According to oxygen vacancy rich layer model and low oxygen vacancy mobility model, we add AlOx layer between TiOx and HfOx for improving the device’s performance. Based on TEM and EDX analyses, we find that Al doped into HfOx layer to form HfAlOx compound film. Based on such the oxygen vacancy mobility of HfAlOx layer formation, would lead to narrow the second filament. Through experiments, 1nm thick AlOx layer employed in the TiN/TiO/HfAlOx/TiN device exhibits the best property. Such device obtains excellent properties such as faster speed device (both set and reset pulse width is 1us) with good nonlinearity (3.39 for potentiation and 2.87 for depression behavior) and best nonlinearity (2.15 for potentiation and 1.52 for depression behavior with 10us pulse width) with 500 conductance states and retention with more than 104 s.