A Physics of Failure, Kinetic Simulation Model for Reliability of RRAM

2023 ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM, RAMS(2023)

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摘要
In this work, a physics-based reliability model (PFKS model) of HfO ffv based resistive random-access memory (RRAM) is developed using Kinetic Monte Carlo (KMC) simulation with MATLAB to demonstrate the correlation between microscopic kinetics, behavioral degradation and variation of RRAM. The resistive switching process of RRAM is simulated through a dynamic resistor network. The physics of oxygen vacancy generation, oxygen ion migration, hopping and recombination is modeled through multiple kinetic processes during switching, including forming, SET, and RESET. The electric field, current temperature distribution, resistance and microstructural development of the dielectric are analyzed simultaneously and updated in time steps. The proposed PFKS model looks into the physical causes of endurance and retention degradations as a function of temperature, number of cycles, and time. The PFKS model is capable of simulating operation cycles, with flexible setups in material properties (adaptable to new materials combinations), structures (adaptable to various setups in device layers), and applied conditions (regarding temperature, voltage profiles, and compliance current). This paper describes the motivation for studying RRAM, and the need for looking into RRAM reliability attributes and characteristic for trade-offs. The workflow and simulation setups of this PFKS model are demonstrated schematically and mathematically. The simulation results for retention and endurance are discussed. The degradation rate of RRAM can be further modified by constructing a Bayesian network of reliability attributes and assigning conditional dependencies. In summary, the proposed PFKS model provides insights on tradeoffs among materials characteristics, structure differences, and operating conditions considering RRAM operation and reliability optimization.
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关键词
Physics of Failure,RRAM,Kinetic Monte Carlo Simulation,Reliability Model,Endurance,Retention
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