Statistical nature of hard breakdown recovery in high-κ dielectric stacks studied using ramped voltage stress

作     者：Feng, X. Raghavan, N. Mei, S. Dong, S. Pey, K. L. Wong, H. 

作者机构：Zhejiang Univ Inst Microelect & Nanoelect Hangzhou 310027 Zhejiang Peoples R China Singapore Univ Technol & Design Engn Prod Dev EPD Pillar Singapore Singapore City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China 

出 版 物：《MICROELECTRONICS RELIABILITY》 (微电子学可靠性)

年 卷 期：2018年第88-90卷

页      面：164-168页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：International Collaborative Research and Communication Scholarship for Zhejiang University (ZJU) Doctoral Students SUTD-ZJU Research Collaboration Grant [ZJURP1300104] SUTD Start-Up Research Grant [SREP15108] 

主　　题：Clustering model Hard breakdown recovery High-kappa dielectric Ramped voltage stress Weibull distribution Weibull slope 

摘      要：In replacing the conventional SiO2 gate dielectric with high-kappa materials, new challenges emerge on understanding the kinetics of dielectric breakdown due to the different properties of the new bulk oxide and the interfacial layers at the substrate and gate electrode interface as well. Among several complexities, dielectric relaxation and recovery have received a lot of attention due to their promising applications in resistive random access memory (RRAM). In this study, we explore the stochastic nature of hard breakdown recovery in HfO2, taking advantage of ramped voltage stress (RVS) measurements, which are theoretically equivalent to the widely used constant voltage stress (CVS), while being significantly less time-consuming. We found that the possibility of recovery is largely dependent on the ramp rate during RVS as the dielectric needs adequate time and sufficient thermal budget to recover. The clustering model is found to be a good fit to the RVS data sets for post-recovery subsequent breakdown events and the extent of defect clustering is found to be more intense after increasing number of recovery events. The breakdown mechanism in the stack is confirmed by measuring the resistance change trends with temperature.