Zirconium oxide-based resistive switching memory for neuromorphic computing applications

• Main Authors: CHEN, KUAN-CHIEH, 陳冠傑
• Other Authors: Tseng, Tseung-Yuen
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/q847ky

碩士 === 國立交通大學 === 電子研究所 === 107 === Resistive random access memory (RRAM) is the most promising nonvolatile memory in the future, due to its serval advantages, low power consumption, high operation speed, and 3D compatible architecture……etc. Another potential application of RRAM is to implement it to neuromorphic computing. To use RRAM as an electronic synapse, it should perform the capability of gradual resistance change. Furthermore, some electrical properties and metrics need to be considered, e.g., linearity, symmetry, dynamic range, etc. Many papers conclude that the higher linearity of resistance change, the better the learning accuracy we can achieve in the machine learning task. In this thesis, we mainly focus on ZrOx-based RRAM, trying to improve the nonlinearity by device design engineering. Firstly, by changing the bottom electrode from Pt to TiN, we successfully demonstrate ZrOx-based RRAM with gradual resistivity change. In addition, we propose a mechanism to explain the difference. Secondly, by introducing additional post-deposition annealing, the nonlinearity of the weight update is further improved from >9 to 4.45 for potentiation; >9 to 5.29 for depression. This can be explained by interface oxygen vacancies due to the formation of the TiON layer after annealing. In the third part, based on the previous report, a methodology to improve the nonlinearity, we used the AlOx as a barrier layer, because AlOx has low ion mobility due to the ALD process. By stacking AlOx under ZrOx, we obtained the bilayer structure RRAM. Compared to the single layer (ZrOx) device, the nonlinearity was further improved to 3.94 and 2.42 for potentiation and depression, respectively, and the methodology was confirmed. Additionally, with process parameter optimized, we have fabricated a synaptic RRAM with high linearity weight update, which nonlinearity is 1.3 for potentiation, and 1.82 for depression. In the future, this can be further applied to the neuromorphic computing system to serve as the electronic synapse.