Towards Successful Application of Phase Change Memories: Addressing Challenges from Write Operations

The emerging Phase Change Memory (PCM) technology is drawing increasing attention due to its advantages in non-volatility, byte-addressability and scalability. It is regarded as a promising candidate for future main memory. However, PCM's write operation has some limitations that pose challenges to its application in memory. The disadvantages include long write latency, high write power and limited write endurance. In this thesis, I present my effort towards successful application of PCM memory. My research consists of several optimizing techniques at both the circuit and architecture level. First, at the circuit level, I propose Differential Write to remove unnecessary bit changes in PCM writes. This is not only beneficial to endurance but also to the energy and latency of writes. Second, I propose two memory scheduling enhancements (AWP and RAWP) for a non-blocking bank design. My memory scheduling enhancements can exploit intra-bank parallelism provided by non-blocking bank design, and achieve significant throughput improvement. Third, I propose Bit Level Power Budgeting (BPB), a fine-grained power budgeting technique that leverages the information from Differential Write to achieve even higher memory throughput under the same power budget. Fourth, I propose techniques to improve the QoS tuning ability of high-priority applications when running on PCM memory. In summary, the techniques I propose effectively address the challenges of PCM's write operations. In addition, I present the experimental infrastructure in this work and my visions of potential future research topics, which could be helpful to other researchers in the area

A DATA AWARE APPROACH TO SALVAGE THE ENDURANCE OF PHASE-CHANGE MEMORY

Phase Change Memory (PCM) is an emerging non-volatile memory technology that could either replace or augment DRAM and NAND flash that are hindered by scalability challenges. PCM suffers from a limited endurance problem that needs to be alleviated before it can be endorsed into the memory stack. This thesis is based on the observation that the endurance problem and its ramification depend on the write data. Accordingly, a data-aware approach is applied to salvage the endurance of PCM at three different stages: pre-write fault avoidance, post-write fault tolerance and post-failure recovery. The pre-write fault avoidance stage aims at reducing the endurance cost of servicing write requests. To this end, Cost Aware Flip Optimization (CAFO) is presented as an efficient technique to lessen the endurance degradation. Essentially, CAFO relies on a cost model that captures the endurance cost of programming memory cells based on their already stored values. Subsequently,the write data is encoded into a form that incurs a lower endurance cost than the original write data. Overall, CAFO is capable of reducing the endurance cost by up to 65% more than the existing schemes. Worn out PCM cells exhibit a stuck-at fault model which makes the manifestation of errors dependent on the values that cells are stuck at. When a write fails, the data is rewritten inverted. This dissertation shows that applying data inversion at the post-write fault tolerance stage exploits the data dependent nature of errors which enables ECCs to tolerate faults up to double their nominal capability. Furthermore, extensions to RDIS which is an ECC designed specifically for the stuck-at fault model are presented. At the post-failure recovery stage, Data Dependent Sparing is presented to manage bad blocks in PCM. Departing from the observation that defective blocks in the context of the stuck-at fault model still exhibit a low write failure probability due to the data dependent nature of errors, this thesis takes the approach of reusing blocks that are defective yet better-than-bad through a dynamic management of the reserve spare space. Data Dependent Sparing is capable of increasing the lifetime of PCM by up to 18%

Enhanced 3-Tier Storage Management Scheme For Flash Memory-Based Solid State Disk

Nowadays, significant attention has been paid to the flash memory-based Solid State Disk (SSD). Different from traditional disks, SSD uses semiconductor chips for storing the data. This structure enjoys original technical characteristics including low power consumption, shock resistance and high performance in random access. Owing to these features, many devices are using flash memory as a basic storage component, such as mp3, smartphones, and tablet devices. However, the flash memory, basic unit of SSD, has many distinctive characteristics, which lead to multifarious challenges for example, writing data (storing data) is only allowed on empty storage unit (block), which makes it more time-consuming

전자 장치 내 국부적 전계 향상을 위한 나노 구조체

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부, 2021.8. 조재영.The goal of this dissertation is to investigate effect of nanostructures for local electric field enhancement in electronic devices and to provide experimental and theoretical bases for their practical use. Resistive random access memory (RRAM) is a data storage device that can be modulated its resistance states by external electrical stimuli. The electric field generated by the applied potential difference between the two electrodes acts as the driving force to switch the resistance states, so controlling the electric field within the device can lead to improved operational performance and reliability of the device. Even though considerable progress has been made through significant efforts to control the electric field within the device, selectively enhancing the electric field in the intended position for stable and uniform resistive switching behavior is still challenging. Engineered metal structures in the RRAM can efficiently manipulate the electric field. As the radius of the metal structures decreases, the charge density increases, generating electric field enhancements in confined region. To minimize the radius of the metal structure and thus to greatly increase the electric field in a local area, we introduced a nanoscale metal structure into the RRAM. First, pyramid-structured metal electrode with a sharp tip was used to achieve a tip-enhanced electric field, and the effect of the enhanced electric field on the resistive switching behaviors of the device was investigated. Based on numerical simulation and experimental results, we confirmed that pyramidal electrode with a tip radius of tens of nanometers can selectively enhance the electric field at the tip. The tip-enhanced electric field can facilitate the thermochemical reaction in transition metal oxide-based RRAMs and efficiency of charge injection and transport in organic-based RRAMs, as well as provide position selectivity during formation of conductive filament. The resulting RRAM exhibited reliable resistive switching behavior and highly improved device performance compared with conventional RRAM with planar electrode. As another approach to enhance the electric field within the resistive switching layer, we prepared spherical nanostructures via self-assembled block copolymer (BCP)/metal compound micelles. BCP and metal precursors were dissolved in aqueous media for use as BCP/metal compound micelles. These micelles were used as complementary resistive switch (CRS) layers of the memory device and the mechanism of CRS behavior was investigated. The spherical metal nanostructures can improve the electric fields, promoting a resistive switching mechanism based on electrochemical metallization. The resulting CRS memory exhibited reliable resistive switching behavior with four distinct threshold voltages in both cycle-to-cycle and cell-to-cell tests. Also, the conduction and resistive switching mechanism are experimentally demonstrated through the the analysis of the current–voltage data plot and detemination of the temperature coefficient of resistance. Overall, we pursued efficient engineering of metal nanostructures capable of manipulating electric fields for improving the operational performance and reliability of memory devices. There is no doubt that the commercialized RRAM will become popular in the near future after overcoming all the challenges of RRAM through continuous interest and research. We believe that these results will not only contribute to the significant advancement of all electronic devices, including RRAM, but will also help promote research activities in the electronic device field.본 논문의 목적은 나노 구조체를 통한 전자 장치 내 국부적 전계 향상 효과를 조사하고, 이의 실제 사용을 위한 실험 및 이론적 기반을 제공하는 것이다. 저항변화메모리 (resistive random access memory) 는 외부 전기 자극에 의해 저항 상태를 변화 시킬 수 있는 데이터 저장 장치이다. 두 전극 사이에 인가된 전위차에 의해 생성된 전기장은 저항 상태를 전환시키는 구동력으로써 작용하므로, 전자 장치 내에서 전기장을 제어하면 장치의 성능과 신뢰성을 향상시킬 수 있다. 장치 내에서 전기장을 제어하려는 많은 노력을 통해 상당한 진전이 있었지만, 안정적이고 균일한 저항 변화 거동을 위해 의도된 위치에서 전기장을 선택적으로 향상시키는 일은 아직 도전적 과제이다. 구조화된 금속을 저항변화메모리에 접목시킴으로써 전기장을 효율적으로 조작할 수 있다. 금속 구조체의 반경이 감소함에 따라 전하 밀도가 증가하여 국부적 영역에서 전기장이 향상된다. 이 논문에서는 금속 구조체의 반경을 최소화하여 국부적으로 전기장을 크게 향상시키기 위해 저항변화메모리에 나노스케일의 금속 구조체를 도입하였다. 첫 번째로, 팁 강화 (tip-enhanced) 전기장 효과를 달성하기 위해 날카로운 팁을 가지는 피라미드 금속 구조체를 전극으로 사용하였으며, 강화된 전기장이 소자의 저항 변화 거동에 미치는 영향을 조사하였다. 유한요소모델링과 실험결과를 바탕으로, 수십 나노 미터의 팁 반경을 가지는 피라미드 구조체 전극이 팁 부근에서 전기장을 국소적으로 향상시킬 수 있음을 확인하였다. 팁 강화 전기장은 전이 금속 산화물-기반 저항변화메모리에서 열화학 (thermochemical) 반응을 촉진시키고 유기-기반 저항변화메모리에서 전하 주입 (charge injection) 및 수송 (transport) 효율성을 향상시킬 뿐 아니라, 선택적인 위치에서만 전도성 필라멘트 (conductive filament)를 형성시킬 수 있었다. 그 결과 피라미드 구조체 저항변화메모리는 종래의 평판 구조체 저항변화메모리에 비해 안정적인 저항 변화 거동과 향상된 장치 성능을 보여주었다. 저항 변화 층 내의 전기장을 향상시키기 위한 또 다른 접근법으로, 자기조립 (self-assembled)된 블록공중합체 (block copolymer)/금속 복합체 미셀 (micelle)을 이용하여 구형의 나노구조체를 소자의 중간층으로 도입하였다. 블록공중합체 및 금속전구체를 복합체 미셀로 사용하기 위해 선택적 용매에 용해시켰다. 해당 미셀을 메모리 소자의 상보적 저항 변화 (complementary resistive switch) 층으로 사용하였으며, 상보적 저항 변화 거동의 메커니즘을 조사하였다. 구형의 금속 나노구조체는 전기장을 향상시켜 전기화학적 금속화 (electrochemical metallization)에 기반한 저항 변화 메커니즘을 촉진시킬 수 있었다. 그 결과 상보적 저항 변화 메모리는 사이클 및 셀간 반복 시험 모두에서 4개의 임계 전압으로 안정적인 저항 변화 동작을 나타내었다. 또한 전류-전압 자료 플롯 (plot) 분석과 저항의 온도 계수 결정을 통해 장치의 전도 및 저항 변화 메커니즘을 실험적으로 입증하였다. 전반적으로 본 논문에서는 장치 내 전기장을 증폭시킬 수 있는 금속 나노구조체의 효율적인 엔지니어링을 통해 메모리 장치의 성능과 신뢰성 향상을 추구하였다. 지속적인 관심과 연구를 통해 저항변화메모리의 모든 과제를 극복한 후, 상용화된 저항변화메모리가 가까운 미래에 대중화될 것임을 믿어 의심치 않는다. 우리는 이 결과가 저항변화메모리를 포함한 모든 전자 장치의 획기적인 발전에 기여할 뿐만 아니라 전자 장치 분야의 연구 활동을 촉진하는 데에도 도움이 될 것이라고 믿는다.Chapter 1. Introduction 1 1.1. Background 1 1.1.1. Necessity of new memory devices 1 1.1.2. Resistive random access memory 2 1.2. Motivation 4 1.3. Dissertation Overview 6 1.4. References 9 Chapter 2. Tip-Enhanced Electric Field-Driven Efficient Charge Injection and Transport in Organic Material-Based Resistive Memories 19 2.1. Introduction 21 2.2. Experimental 24 2.3. Results and Discussion 27 2.4. Conclusions 37 2.5. References 38 Chapter 3. Facilitation of the Thermochemical Mechanism in NiO-Based Resistive Switching Memories via Tip-Enhanced Electric Fields 52 3.1. Introduction 54 3.2. Experimental 57 3.3. Results and Discussion 60 3.4. Conclusions 66 3.5. References 67 Chapter 4. Facile Achievement of Complementary Resistive Switching Behaviors via Self-Assembled Block Copolymer Micelles 82 4.1. Introduction 83 4.2. Experimental 86 4.3. Results and Discussion 89 4.4. Conclusions 96 4.5. References 97 Chapter 5. Conclusion 109 Abstract in Korean 112박

Nanoparticle Engineering for Chemical-Mechanical Planarization

Increasing reliance on electronic devices demands products with high performance and efficiency. Such devices can be realized through the advent of nanoparticle technology. This book explains the physicochemical properties of nanoparticles according to each step in the chemical mechanical planarization (CMP) process, including dielectric CMP, shallow trend isolation CMP, metal CMP, poly isolation CMP, and noble metal CMP. The authors provide a detailed guide to nanoparticle engineering of novel CMP slurry for next-generation nanoscale devices below the 60nm design rule. This comprehensive text also presents design techniques using polymeric additives to improve CMP performance

Design and Code Optimization for Systems with Next-generation Racetrack Memories

With the rise of computationally expensive application domains such as machine learning, genomics, and fluids simulation, the quest for performance and energy-efficient computing has gained unprecedented momentum. The significant increase in computing and memory devices in modern systems has resulted in an unsustainable surge in energy consumption, a substantial portion of which is attributed to the memory system. The scaling of conventional memory technologies and their suitability for the next-generation system is also questionable. This has led to the emergence and rise of nonvolatile memory ( NVM ) technologies. Today, in different development stages, several NVM technologies are competing for their rapid access to the market. Racetrack memory ( RTM ) is one such nonvolatile memory technology that promises SRAM -comparable latency, reduced energy consumption, and unprecedented density compared to other technologies. However, racetrack memory ( RTM ) is sequential in nature, i.e., data in an RTM cell needs to be shifted to an access port before it can be accessed. These shift operations incur performance and energy penalties. An ideal RTM , requiring at most one shift per access, can easily outperform SRAM . However, in the worst-cast shifting scenario, RTM can be an order of magnitude slower than SRAM . This thesis presents an overview of the RTM device physics, its evolution, strengths and challenges, and its application in the memory subsystem. We develop tools that allow the programmability and modeling of RTM -based systems. For shifts minimization, we propose a set of techniques including optimal, near-optimal, and evolutionary algorithms for efficient scalar and instruction placement in RTMs . For array accesses, we explore schedule and layout transformations that eliminate the longer overhead shifts in RTMs . We present an automatic compilation framework that analyzes static control flow programs and transforms the loop traversal order and memory layout to maximize accesses to consecutive RTM locations and minimize shifts. We develop a simulation framework called RTSim that models various RTM parameters and enables accurate architectural level simulation. Finally, to demonstrate the RTM potential in non-Von-Neumann in-memory computing paradigms, we exploit its device attributes to implement logic and arithmetic operations. As a concrete use-case, we implement an entire hyperdimensional computing framework in RTM to accelerate the language recognition problem. Our evaluation shows considerable performance and energy improvements compared to conventional Von-Neumann models and state-of-the-art accelerators

Quantum and spin-based tunneling devices for memory systems

Rapid developments in information technology, such as internet, portable computing, and wireless communication, create a huge demand for fast and reliable ways to store and process information. Thus far, this need has been paralleled with the revolution in solid-state memory technologies. Memory devices, such as SRAM, DRAM, and flash, have been widely used in most electronic products. The primary strategy to keep up the trend is miniaturization. CMOS devices have been scaled down beyond sub-45 nm, the size of only a few atomic layers. Scaling, however, will soon reach the physical limitation of the material and cease to yield the desired enhancement in device performance. In this thesis, an alternative method to scaling is proposed and successfully realized. The proposed scheme integrates quantum devices, Si/SiGe resonant interband tunnel diodes (RITD), with classical CMOS devices forming a microsystem of disparate devices to achieve higher performance as well as higher density. The device/circuit designs, layouts and masks involving 12 levels were fabricated utilizing a process that incorporates nearly a hundred processing steps. Utilizing unique characteristics of each component, a low-power tunneling-based static random access memory (TSRAM) has been demonstrated. The TSRAM cells exhibit bistability operation with a power supply voltage as low as 0.37 V. Various TSRAM cells were also constructed and their latching mechanisms have been extensively investigated. In addition, the operation margins of TSRAM cells are evaluated based on different device structures and temperature variation from room temperature up to 200oC. The versatility of TSRAM is extended beyond the binary system. Using multi-peak Si/SiGe RITD, various multi-valued TSRAM (MV-TSRAM) configurations that can store more than two logic levels per cell are demonstrated. By this virtue, memory density can be substantially increased. Using two novel methods via ambipolar operation and utilization of enable/disable transistors, a six-valued MV-TSRAM cell are demonstrated. A revolutionary novel concept of integrating of Si/SiGe RITD with spin tunnel devices, magnetic tunnel junctions (MTJ), has been developed. This hybrid approach adds non-volatility and multi-valued memory potential as demonstrated by theoretical predictions and simulations. The challenges of physically fabricating these devices have been identified. These include process compatibility and device design. A test bed approach of fabricating RITD-MTJ structures has been developed. In conclusion, this body of work has created a sound foundation for new research frontiers in four different major areas: integrated TSRAM system, MV-TSRAM system, MTJ/RITD-based nonvolatile MRAM, and RITD/CMOS logic circuits

ENERGY-AWARE OPTIMIZATION FOR EMBEDDED SYSTEMS WITH CHIP MULTIPROCESSOR AND PHASE-CHANGE MEMORY

Over the last two decades, functions of the embedded systems have evolved from simple real-time control and monitoring to more complicated services. Embedded systems equipped with powerful chips can provide the performance that computationally demanding information processing applications need. However, due to the power issue, the easy way to gain increasing performance by scaling up chip frequencies is no longer feasible. Recently, low-power architecture designs have been the main trend in embedded system designs. In this dissertation, we present our approaches to attack the energy-related issues in embedded system designs, such as thermal issues in the 3D chip multiprocessor (CMP), the endurance issue in the phase-change memory(PCM), the battery issue in the embedded system designs, the impact of inaccurate information in embedded system, and the cloud computing to move the workload to remote cloud computing facilities. We propose a real-time constrained task scheduling method to reduce peak temperature on a 3D CMP, including an online 3D CMP temperature prediction model and a set of algorithm for scheduling tasks to different cores in order to minimize the peak temperature on chip. To address the challenging issues in applying PCM in embedded systems, we propose a PCM main memory optimization mechanism through the utilization of the scratch pad memory (SPM). Furthermore, we propose an MLC/SLC configuration optimization algorithm to enhance the efficiency of the hybrid DRAM + PCM memory. We also propose an energy-aware task scheduling algorithm for parallel computing in mobile systems powered by batteries. When scheduling tasks in embedded systems, we make the scheduling decisions based on information, such as estimated execution time of tasks. Therefore, we design an evaluation method for impacts of inaccurate information on the resource allocation in embedded systems. Finally, in order to move workload from embedded systems to remote cloud computing facility, we present a resource optimization mechanism in heterogeneous federated multi-cloud systems. And we also propose two online dynamic algorithms for resource allocation and task scheduling. We consider the resource contention in the task scheduling

Facilitating Emerging Non-volatile Memories in Next-Generation Memory System Design: Architecture-Level and Application-Level Perspectives

This dissertation focuses on three types of emerging NVMs, spin-transfer torque RAM (STT-RAM), phase change memory (PCM), and metal-oxide resistive RAM (ReRAM). STT-RAM has been identified as the best replacement of SRAM to build large-scale and low-power on-chip caches and also an energy-efficient alternative to DRAM as main memory. PCM and ReRAM have been considered to be promising technologies for building future large-scale and low-power main memory systems. This dissertation investigates two aspects to facilitate them in next-generation memory system design, architecture-level and application-level perspectives. First, multi-level cell (MLC) STT-RAM based cache design is optimized by using data encoding and data compression. Second, MLC STT-RAM is utilized as persistent main memory for fast and energy-efficient local checkpointing. Third, the commonly used database indexing algorithm, B+tree, is redesigned to be NVM-friendly. Forth, a novel processing-in-memory architecture built on ReRAM based main memory is proposed to accelerate neural network applications

NASA Tech Briefs, October 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical' Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences