RRAM variability and its mitigation schemes

Emerging technologies such as RRAMs are attracting significant attention due to their tempting characteristics such as high scalability, CMOS compatibility and non-volatility to replace the current conventional memories. However, critical causes of hardware reliability failures, such as process variation due to their nano-scale structure have gained considerable importance for acceptable memory yields. Such vulnerabilities make it essential to investigate new robust design strategies at the circuit system level. In this paper we have analyzed the RRAM variability phenomenon, its impact and variation tolerant techniques at the circuit level. Finally a variation-monitoring circuit is presented that discerns the reliable memory cells affected by process variability.Peer ReviewedPostprint (author's final draft

Treated HfO2 based rram devices with ru, tan, tin as top electrode for in-memory computing hardware

The scalability and power efficiency of the conventional CMOS technology is steadily coming to a halt due to increasing problems and challenges in fabrication technology. Many non-volatile memory devices have emerged recently to meet the scaling challenges. Memory devices such as RRAMs or ReRAM (Resistive Random-Access Memory) have proved to be a promising candidate for analog in memory computing applications related to inference and learning in artificial intelligence. A RRAM cell has a MIM (Metal insulator metal) structure that exhibits reversible resistive switching on application of positive or negative voltage. But detailed studies on the power consumption, repeatability and retention of during multi-level operation have not been undertaken previously. Transition metal oxide-based RRAMs, using HfO2, executes change in resistance (switching behavior) via electrochemical migration of oxygen vacancies. This thesis investigates the role of extra oxygen vacancies, introduced by plasma exposure (treated), in HfO2 to reduce the power consumption of RRAM. In addition to oxygen vacancy rich HfO2, various top metal electrodes including Ruthenium (Ru) are explored to enhance the switching behavior and power consumption. Use of Ru as a top metal reduced the switching energy of the treated HfO2 RRAM device

Characterization of low power HfO2 based switching devices for in-memory computing

Oxide based Resistive Random Access Memory (RRAM) devices are investigated as one of the promising non-volatile memories to be used for in-memory computing that will replace the classical von Neumann architecture and reduce the power consumption. These applications required multilevel cell (MLC) characteristics that can be achieved in RRAM devices. One of the methods to achieve this analog switching behavior is by performing an optimized electrical pulse. The RRAM device structure is basically an insulator between two metals as metal-insulator-metal (MIM) structure. Where one of the primary challenges is to assign an RRAM stack with both low power consumption and good switching performance. This thesis investigates different HfO2 based RRAM stacks and compares their electrical and MLC characteristics. By engineering the distribution of defects and oxygen vacancies in the switching layer, which have been done by exposing the dielectric with a hydrogen plasma treatment in the first device, using HfO2 and Al2O3 as a bilayer, or by adding Zr to the HfO2. While the plasma treated devices show a promising conductance quantization with low power consumption, the performance can be further enhanced by engineering the bottom electrode. The impact of introducing additional nitrogen at the bottom electrode, TiN, shows additional reduction in the switching power of the plasma treated devices

Analysis on the Filament Structure Evolution in Reset Transition of Cu/HfO2/Pt RRAM Device

The resistive switching (RS) process of resistive random access memory (RRAM) is dynamically correlated with the evolution process of conductive path or conductive filament (CF) during its breakdown (rupture) and recovery (reformation). In this study, a statistical evaluation method is developed to analyze the filament structure evolution process in the reset operation of Cu/HfO₂/Pt RRAM device. This method is based on a specific functional relationship between the Weibull slopes of reset parameters' distributions and the CF resistance (R on). The CF of the Cu/HfO₂/Pt device is demonstrated to be ruptured abruptly, and the CF structure of the device has completely degraded in the reset point. Since no intermediate states are generated in the abrupt reset process, it is quite favorable for the reliable and stable one-bit operation in RRAM device. Finally, on the basis of the cell-based analytical thermal dissolution model, a Monte Carlo (MC) simulation is implemented to further verify the experimental results. This work provides inspiration for RRAM reliability and performance design to put RRAM into practical application

Simulation and implementation of novel deep learning hardware architectures for resource constrained devices

Corey Lammie designed mixed signal memristive-complementary metal–oxide–semiconductor (CMOS) and field programmable gate arrays (FPGA) hardware architectures, which were used to reduce the power and resource requirements of Deep Learning (DL) systems; both during inference and training. Disruptive design methodologies, such as those explored in this thesis, can be used to facilitate the design of next-generation DL systems

Advanced physical modeling of SiOx resistive random access memories

We apply a three-dimensional (3D) physical simulator, coupling self-consistently stochastic kinetic Monte Carlo descriptions of ion and electron transport, to investigate switching in silicon-rich silica (SiOx) redox-based resistive random-access memory (RRAM) devices. We explain the intrinsic nature of resistance switching of the SiOx layer, and demonstrate the impact of self-heating effects and the initial vacancy distributions on switching. We also highlight the necessity of using 3D physical modelling to predict correctly the switching behavior. The simulation framework is useful for exploring the little-known physics of SiOx RRAMs and RRAM devices in general. This proves useful in achieving efficient device and circuit designs, in terms of performance, variability and reliability