标题: 电阻式记忆体及SONOS快闪式记忆体中介电层缺陷造成之可靠度效应研究
Traps Induced Reliability Issues in Resistive Random Access Memory and SONOS Flash Memory
作者: 钟岳庭
汪大晖
Chung, Yueh-Ting
Wang, Tahui
电子工程学系 电子研究所
关键字: 电阻式记忆体;设定干扰失败时间;衰退;过度设定;缺陷生成;重设失败;蒙地卡罗模拟;操作循环施压;阻值视窗;设定干扰电压;穿隧效应;临界电压维持分布;数值模型;RRAM;SET-disturb failure time;degradation;over-SET;trap generation;RESET failure;Monte-Carlo simulation;cycling stress;resistance window;SET-disturb voltage;SONOS;percolation;Vt retention distribution;numerical model
公开日期: 2016
摘要: 本论文主要探讨电阻式记忆体(RRAM)以及SONOS快闪式记忆体之各种由介电层缺陷造成之可靠度效应。其中包含RRAM中设定干扰错误时间(SET-disturb failure time)、随机电报杂讯(RTN)以及SONOS中单电子造成临界电压(Vt)维持流失之统计量测。蒙地卡罗及统计数值模型亦在此论文中建立以验证相关理论及实验结果。

第一章首先介绍近年来非挥发性记忆体之演进发展以及遇到之各种主要可靠度议题。另外在目前在RRAM交叉式阵列(crossbar array)中会遇到之可靠度议题也将在此章节说明。吾人亦会指出单电子现象在SONOS快闪式记忆体中之影响。最后,此论文之整体架构将在此章节中介绍。

第二章中,吾人发现在钨介电质RRAM中一种写入干扰失败时间(write-disturb failure time)新的衰退机制,且此机制与设定(SET)/重设(RESET)操作循环有关。高阻态元件在某一特定次数之设定/重设操作循环时,写入干扰时间会突然大幅下降数个量级数。虽然记忆体视窗仍然保持,但却可观测到偶发性之过度重设。为了进一步研究此衰退现象,吾人在高阻态下利用定电压施压并量测施压造成漏电流及低频杂讯以观测缺陷在RRAM介电质中生成情形。定电压施压可仿真出设定/重设操作循环时高电压施压造成缺陷生成现象。吾人发现在高阻态断开区中,不论是在定电压施压或设定/重设操作循环施压,低电场下之缺陷帮助穿隧现象将逐渐增强。而高电场施压产生之缺陷与设定产生之氧空缺不同,无法被重设操作抹除,且此种缺陷将会造成重设耐受性失效。吾人建立了三维穿隧效应机制之蒙地卡罗模型以模拟设定干扰失败时间。此模拟同时包含高电场施压生成之缺陷及设定干扰所产成之氧空缺,亦成功解释此种突发式设定干扰时间大幅下降现象是由于高电场施压缺陷产生导通路径所造成。

另外两种会影响设定干扰失败时间之机制,即电阻视窗和设定干扰电压,将在第三章中提出。吾人发现低阻态之电流准位将强烈影响设定干扰失败时间,且此现象是由于设定干扰失败时间之Weibull斜率很小所造成。此外,吾人利用统计量测方式探讨设定干扰电压与设定干扰时间之关系。

在第四章,吾人在铪介电质RRAM中对二阶RTN振幅分布做了统计量测。与低阻态相比,RTN振幅统计分布在高阻态时有一大振幅tail。吾人在各种读取偏压下量测电子捕捉时间与释放时间,并萃取RTN缺陷在铪介电质中之位置,发现缺陷位置与RTN振幅大小有关。由于在重设操作后,氧空缺在断开区并非均匀分布,故可推估高阻态下之大振福RTN tail为靠近阴极电击所造成。

第五章探讨在SONOS中临界电压维持分布tail效应。吾人在NOR型态多阶(multi-level)SONOS量测单电荷流失所造成之临界电压变化。实验结果显示: (1) 单电荷流失造成之临界电压变化呈现一指数分布,且归因于随机写入电荷形成之电流穿隧效应。(2) 在多阶SONOS中,此指数分布之标准差会随着写入电荷数量或写入临界电压等级上升而上升。此外,吾人量测一512Mb数次写入SONOS元件之临界电压维持分布,并发现此分布有一明显tail。一包含穿隧效应及Poisson分布之多电荷流失数值模型亦在此章节中被建立。此模型可成功模拟出512Mb SONOS中所观测到之tail现象,且此tail效应可用电子穿隧效应解释。

最后,第六章为本论文之结论及未来预计之研究方向。
This dissertation will focus on major reliability issues in random access memory (RRAM) and SONOS flash memory induced by traps in a dielectric. Statistical characterization of SET-disturb failure time in an RRAM crossbar array, random telegraph noise (RTN) and single program charge induced Vt retention loss in SONOS are performed. Monte-Carlo simulation model and numerical simulation model are also developed to corroborate our characterization results.

In Chapter 1, first, the evolution of the nonvolatile memory technology in recent years and the major reliability concerns are addressed. Second, the applications and the reliability issues of an RRAM crossbar array will be demonstrated. Also, the impact of single charge phenomenon in SONOS flash memory will be pointed out. The organization of this dissertation will be given in this chapter.

In Chapter 2, a new degradation mode with respect to write-disturb failure time due to SET/RESET cycling in a tungsten oxide resistive random access memory is reported. In a crossbar array memory, we find that a write-disturb failure time in high resistance state reduces suddenly by several orders of magnitude after certain SET/RESET cycles. Although a memory window still remains after the degradation, the occurrence probability of over-SET state increases significantly. To investigate this new degradation mode, we perform constant voltage stress in HRS to characterize trap generation in a switching dielectric by measuring a stress-induced leakage current and low-frequency noise. The constant voltage stress is to emulate high-field stress and thus trap creation in SET/RESET cycling. We find that a low-field current in HRS via trap-assisted tunneling in a rupture region increases gradually in both constant voltage stress and SET/RESET cycling stress. The high-field stress-generated traps, unlike SET-induced oxygen vacancies, cannot be annihilated by RESET operation and are held responsible for a RESET endurance failure. A three dimensional Monte Carlo model based on a percolation concept of oxide breakdown is developed to simulate a SET-disturb failure time. Our model includes both stress-generated traps and SET-disturb induced oxygen vacancies. The model can well explain observed abrupt and drastic SET-disturb lifetime degradation, which is attributed to the formation of a conductive percolation path of stress-generated traps.

In Chapter 3, two more factors affecting SET-disturb failure time (f) including resistance window in operation and SET-disturb voltage are investigated. The dependence of f on resistance window in operation is characterized. We find that f is greatly affected by the current level of LRS. The strong LRS dependence of f is attributed to a small Weibull slope of f. In addition, we perform statistical characterizations of f at different SET-disturb voltages. A relationship between f and a SET-disturb voltage in a stressed cell is given.

Statistical characterization of two-level random telegraph noise (RTN) amplitude distribution in a hafnium oxide resistive memory has been performed in Chapter 4. We find that two-level RTN in HRS exhibits a large amplitude distribution tail, as compared to LRS. To investigate an RTN trap position in a hafnium oxide film, we measure the dependence of electron capture and emission times of RTN on applied read voltage. A correlation between an RTN trap position and RTN amplitude is found. Owing to a non-uniform distribution of oxygen vacancy after a RESET process, RTN traps near the cathode are responsible for an RTN large-amplitude tail in HRS mostly.

In Chapter 5, a Vt retention distribution tail in a Multi-Time-Program (MTP) SONOS memory is investigated. We characterize a single program charge loss induced Vt in NOR-type multi-level SONOS cells (MLC). Our measurement shows that (i) a single charge loss induced Vt exhibits an exponential distribution in magnitudes, which is attributed to a random program charge induced current path percolation effect and (ii) the standard deviation of the exponential distribution depends on a program charge density and increases with a program Vt level in a MLC SONOS. In addition, we measure a Vt retention distribution in a 512Mb MTP SONOS memory and observe a significant Vt retention tail. A numerical Vt retention distribution model including the percolation effect and a Poisson distribution based multiple charge loss model is developed. Our model agrees with the measured Vt retention distribution in a 512Mb SONOS well. The observed Vt tail is realized mainly due to the percolation effect.

Finally, conclusions are made and future work is described in Chapter 6.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT079911808
http://hdl.handle.net/11536/138770
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