Flash Memory Devices

Flash memory devices have represented a breakthrough in storage since their inception in the mid-1980s, and innovation is still ongoing. The peculiarity of such technology is an inherent flexibility in terms of performance and integration density according to the architecture devised for integration. The NOR Flash technology is still the workhorse of many code storage applications in the embedded world, ranging from microcontrollers for automotive environment to IoT smart devices. Their usage is also forecasted to be fundamental in emerging AI edge scenario. On the contrary, when massive data storage is required, NAND Flash memories are necessary to have in a system. You can find NAND Flash in USB sticks, cards, but most of all in Solid-State Drives (SSDs). Since SSDs are extremely demanding in terms of storage capacity, they fueled a new wave of innovation, namely the 3D architecture. Today “3D” means that multiple layers of memory cells are manufactured within the same piece of silicon, easily reaching a terabit capacity. So far, Flash architectures have always been based on "floating gate," where the information is stored by injecting electrons in a piece of polysilicon surrounded by oxide. On the contrary, emerging concepts are based on "charge trap" cells. In summary, flash memory devices represent the largest landscape of storage devices, and we expect more advancements in the coming years. This will require a lot of innovation in process technology, materials, circuit design, flash management algorithms, Error Correction Code and, finally, system co-design for new applications such as AI and security enforcement

Designs for increasing reliability while reducing energy and increasing lifetime

In the last decades, the computing technology experienced tremendous developments. For instance, transistors' feature size shrank to half at every two years as consistently from the first time Moore stated his law. Consequently, number of transistors and core count per chip doubles at each generation. Similarly, petascale systems that have the capability of processing more than one billion calculation per second have been developed. As a matter of fact, exascale systems are predicted to be available at year 2020. However, these developments in computer systems face a reliability wall. For instance, transistor feature sizes are getting so small that it becomes easier for high-energy particles to temporarily flip the state of a memory cell from 1-to-0 or 0-to-1. Also, even if we assume that fault-rate per transistor stays constant with scaling, the increase in total transistor and core count per chip will significantly increase the number of faults for future desktop and exascale systems. Moreover, circuit ageing is exacerbated due to increased manufacturing variability and thermal stresses, therefore, lifetime of processor structures are becoming shorter. On the other side, due to the limited power budget of the computer systems such that mobile devices, it is attractive to scale down the voltage. However, when the voltage level scales to beyond the safe margin especially to the ultra-low level, the error rate increases drastically. Nevertheless, new memory technologies such as NAND flashes present only limited amount of nominal lifetime, and when they exceed this lifetime, they can not guarantee storing of the data correctly leading to data retention problems. Due to these issues, reliability became a first-class design constraint for contemporary computing in addition to power and performance. Moreover, reliability even plays increasingly important role when computer systems process sensitive and life-critical information such as health records, financial information, power regulation, transportation, etc. In this thesis, we present several different reliability designs for detecting and correcting errors occurring in processor pipelines, L1 caches and non-volatile NAND flash memories due to various reasons. We design reliability solutions in order to serve three main purposes. Our first goal is to improve the reliability of computer systems by detecting and correcting random and non-predictable errors such as bit flips or ageing errors. Second, we aim to reduce the energy consumption of the computer systems by allowing them to operate reliably at ultra-low voltage level. Third, we target to increase the lifetime of new memory technologies by implementing efficient and low-cost reliability schemes

Design of High Performance SRAM Based Memory Chip

The semiconductor memory SRAM uses bi-stable latch circuit to store the logic data 1 or 0. It differs from Dynamic RAM (DRAM) which needs periodic refreshment operation for the storage of logic data. Depending upon the frequency of operation SRAM power consumption varies i.e. it consumes very high power at higher frequencies like DRAM. The Cache memory present in the microprocessor needs high speed memory hence SRAM can be used for that purpose in microprocessors. The DRAM is normally used in the Main memory of processors, where importance is given to the density than its speed. The SRAM is also used in industrial subsystems, scientific and automotive electronics. In this thesis 16-Kb Memory is designed by using memory banking method in UMC 90nm technology ,which operates at a frequency of 1GHz.The post layout simulation for the complete design is performed and also obtained power analysis for the overall design. All peripherals like pre-charge, Row Decoder, Word line driver, Sense amplifier, Column Decoder/Mux and write driver are designed and layouts of all the above peripherals also drawn in an optimised manner such that their layout occupies minimum area. The 6T SRAM cell is designed with operating frequency of 8 GHz and stability analysis are also performed for single SRAM cell. The layout of Single SRAM cell is drawn in a symmetric manner, such that two adjacent cells can share same contact, which results reduction in the area of cell layout. The Static Noise Margin, Read noise margin and Write Noise Margin of single cell are found to be 240mV, 115mV and 425mV respectively for a supply voltage of 1V.The effect of pull-up ratio and cell ratio on the stability of SRAM cell is observed

Gestión de jerarquías de memoria híbridas a nivel de sistema

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadoras y Automática y de Ku Leuven, Arenberg Doctoral School, Faculty of Engineering Science, leída el 11/05/2017.In electronics and computer science, the term ‘memory’ generally refers to devices that are used to store information that we use in various appliances ranging from our PCs to all hand-held devices, smart appliances etc. Primary/main memory is used for storage systems that function at a high speed (i.e. RAM). The primary memory is often associated with addressable semiconductor memory, i.e. integrated circuits consisting of silicon-based transistors, used for example as primary memory but also other purposes in computers and other digital electronic devices. The secondary/auxiliary memory, in comparison provides program and data storage that is slower to access but offers larger capacity. Examples include external hard drives, portable flash drives, CDs, and DVDs. These devices and media must be either plugged in or inserted into a computer in order to be accessed by the system. Since secondary storage technology is not always connected to the computer, it is commonly used for backing up data. The term storage is often used to describe secondary memory. Secondary memory stores a large amount of data at lesser cost per byte than primary memory; this makes secondary storage about two orders of magnitude less expensive than primary storage. There are two main types of semiconductor memory: volatile and nonvolatile. Examples of non-volatile memory are ‘Flash’ memory (sometimes used as secondary, sometimes primary computer memory) and ROM/PROM/EPROM/EEPROM memory (used for firmware such as boot programs). Examples of volatile memory are primary memory (typically dynamic RAM, DRAM), and fast CPU cache memory (typically static RAM, SRAM, which is fast but energy-consuming and offer lower memory capacity per are a unit than DRAM). Non-volatile memory technologies in Si-based electronics date back to the 1990s. Flash memory is widely used in consumer electronic products such as cellphones and music players and NAND Flash-based solid-state disks (SSDs) are increasingly displacing hard disk drives as the primary storage device in laptops, desktops, and even data centers. The integration limit of Flash memories is approaching, and many new types of memory to replace conventional Flash memories have been proposed. The rapid increase of leakage currents in Silicon CMOS transistors with scaling poses a big challenge for the integration of SRAM memories. There is also the case of susceptibility to read/write failure with low power schemes. As a result of this, over the past decade, there has been an extensive pooling of time, resources and effort towards developing emerging memory technologies like Resistive RAM (ReRAM/RRAM), STT-MRAM, Domain Wall Memory and Phase Change Memory(PRAM). Emerging non-volatile memory technologies promise new memories to store more data at less cost than the expensive-to build silicon chips used by popular consumer gadgets including digital cameras, cell phones and portable music players. These new memory technologies combine the speed of static random-access memory (SRAM), the density of dynamic random-access memory (DRAM), and the non-volatility of Flash memory and so become very attractive as another possibility for future memory hierarchies. The research and information on these Non-Volatile Memory (NVM) technologies has matured over the last decade. These NVMs are now being explored thoroughly nowadays as viable replacements for conventional SRAM based memories even for the higher levels of the memory hierarchy. Many other new classes of emerging memory technologies such as transparent and plastic, three-dimensional(3-D), and quantum dot memory technologies have also gained tremendous popularity in recent years...En el campo de la informática, el término ‘memoria’ se refiere generalmente a dispositivos que son usados para almacenar información que posteriormente será usada en diversos dispositivos, desde computadoras personales (PC), móviles, dispositivos inteligentes, etc. La memoria principal del sistema se utiliza para almacenar los datos e instrucciones de los procesos que se encuentre en ejecución, por lo que se requiere que funcionen a alta velocidad (por ejemplo, DRAM). La memoria principal está implementada habitualmente mediante memorias semiconductoras direccionables, siendo DRAM y SRAM los principales exponentes. Por otro lado, la memoria auxiliar o secundaria proporciona almacenaje(para ficheros, por ejemplo); es más lenta pero ofrece una mayor capacidad. Ejemplos típicos de memoria secundaria son discos duros, memorias flash portables, CDs y DVDs. Debido a que estos dispositivos no necesitan estar conectados a la computadora de forma permanente, son muy utilizados para almacenar copias de seguridad. La memoria secundaria almacena una gran cantidad de datos aun coste menor por bit que la memoria principal, siendo habitualmente dos órdenes de magnitud más barata que la memoria primaria. Existen dos tipos de memorias de tipo semiconductor: volátiles y no volátiles. Ejemplos de memorias no volátiles son las memorias Flash (algunas veces usadas como memoria secundaria y otras veces como memoria principal) y memorias ROM/PROM/EPROM/EEPROM (usadas para firmware como programas de arranque). Ejemplos de memoria volátil son las memorias DRAM (RAM dinámica), actualmente la opción predominante a la hora de implementar la memoria principal, y las memorias SRAM (RAM estática) más rápida y costosa, utilizada para los diferentes niveles de cache. Las tecnologías de memorias no volátiles basadas en electrónica de silicio se remontan a la década de1990. Una variante de memoria de almacenaje por carga denominada como memoria Flash es mundialmente usada en productos electrónicos de consumo como telefonía móvil y reproductores de música mientras NAND Flash solid state disks(SSDs) están progresivamente desplazando a los dispositivos de disco duro como principal unidad de almacenamiento en computadoras portátiles, de escritorio e incluso en centros de datos. En la actualidad, hay varios factores que amenazan la actual predominancia de memorias semiconductoras basadas en cargas (capacitivas). Por un lado, se está alcanzando el límite de integración de las memorias Flash, lo que compromete su escalado en el medio plazo. Por otra parte, el fuerte incremento de las corrientes de fuga de los transistores de silicio CMOS actuales, supone un enorme desafío para la integración de memorias SRAM. Asimismo, estas memorias son cada vez más susceptibles a fallos de lectura/escritura en diseños de bajo consumo. Como resultado de estos problemas, que se agravan con cada nueva generación tecnológica, en los últimos años se han intensificado los esfuerzos para desarrollar nuevas tecnologías que reemplacen o al menos complementen a las actuales. Los transistores de efecto campo eléctrico ferroso (FeFET en sus siglas en inglés) se consideran una de las alternativas más prometedores para sustituir tanto a Flash (por su mayor densidad) como a DRAM (por su mayor velocidad), pero aún está en una fase muy inicial de su desarrollo. Hay otras tecnologías algo más maduras, en el ámbito de las memorias RAM resistivas, entre las que cabe destacar ReRAM (o RRAM), STT-RAM, Domain Wall Memory y Phase Change Memory (PRAM)...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Achieving Reliable and Sustainable Next-Generation Memories

Conventional memory technology scaling has introduced reliability challenges due to dysfunctional, improperly formed cells and crosstalk from increased cell proximity. Furthermore, as the manufacturing effort becomes increasingly complex due to these deeply scaled technologies, holistic sustainability is negatively impacted. The development of new memory technologies can help overcome the capacitor scaling limitations of DRAM. However, these technologies have their own reliability concerns, such as limited write endurance in the case of Phase Change Memories (PCM). Moreover, emerging system requirements, such as in-memory encryption to protect sensitive or private data and operation in harsh environments create additional challenges that must be addressed in the context of reliability and sustainability. This dissertation provides new multifactor and ultimately unified solutions to address many of these concerns in the same system. In particular, my contributions toward mitigating these issues are as follows. I present GreenChip and GreenAsic, which together provide the first tools to holistically evaluate new computer architecture, chip, and memory design concepts for sustainability. These tools provide detailed estimates of manufacturing and operational-phase metrics for different computing workloads and deployment scenarios. Using GreenChip, I examined existing DRAM reliability techniques in the context of their holistic sustainability impact, including my own technique to mitigate bitline crosstalk. For PCM, I provided a new reliability technique with no additional storage overhead that substantially increases the lifetime of an encrypted memory system. To provide bit-level error correction, I developed compact linked-list and Bloom-filter-based bit-level fault map structures, that provide unprecedented levels of error tabulation, combined with my own novel error correction and lifetime extension approaches based on these maps for less area than traditional ECC. In particular, FaME, can correct N faults using N bits when utilizing a bit-level fault map. For operation in harsh environments, I created a triple modular redundancy (TMR) pointer-based fault map, HOTH, which specifically protects cells shown to be weak to radiation. Finally, to combine the analyses of holistic sustainability and memory lifetime, I created the LARS technique, which adjusts the GreenChip indifference analysis to account for the additional sustainability benefit provided by increased reliability and lifetime

Algorithm and Hardware Co-Design for Local/Edge Computing

Advances in VLSI manufacturing and design technology over the decades have created many computing paradigms for disparate computing needs. With concerns for transmission cost, security, latency of centralized computing, edge/local computing are increasingly prevalent in the faster growing sectors like Internet-of-Things (IoT) and other sectors that require energy/connectivity autonomous systems such as biomedical and industrial applications. Energy and power efficient are the main design constraints in local and edge computing. While there exists a wide range of low power design techniques, they are often underutilized in custom circuit designs as the algorithms are developed independent of the hardware. Such compartmentalized design approach fails to take advantage of the many compatible algorithmic and hardware techniques that can improve the efficiency of the entire system. Algorithm hardware co-design is to explore the design space with whole stack awareness. The main goal of the algorithm hardware co-design methodology is the enablement and improvement of small form factor edge and local VLSI systems operating under strict constraints of area and energy efficiency. This thesis presents selected works of application specific digital and mixed-signal integrated circuit designs. The application space ranges from implantable biomedical devices to edge machine learning acceleration

Circuit design for embedded memory in low-power integrated circuits

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 141-152).This thesis explores the challenges for integrating embedded static random access memory (SRAM) and non-volatile memory-based on ferroelectric capacitor technology-into lowpower integrated circuits. First considered is the impact of process variation in deep-submicron technologies on SRAM, which must exhibit higher density and performance at increased levels of integration with every new semiconductor generation. Techniques to speed up the statistical analysis of physical memory designs by a factor of 100 to 10,000 relative to the conventional Monte Carlo Method are developed. The proposed methods build upon the Importance Sampling simulation algorithm and efficiently explore the sample space of transistor parameter fluctuation. Process variation in SRAM at low-voltage is further investigated experimentally with a 512kb 8T SRAM test chip in 45nm SOI CMOS technology. For active operation, an AC coupled sense amplifier and regenerative global bitline scheme are designed to operate at the limit of on current and off current separation on a single-ended SRAM bitline. The SRAM operates from 1.2 V down to 0.57 V with access times from 400ps to 3.4ns. For standby power, a data retention voltage sensor predicts the mismatch-limited minimum supply voltage without corrupting the contents of the memory. The leakage power of SRAM forces the chip designer to seek non-volatile memory in applications such as portable electronics that retain significant quantities of data over long durations. In this scenario, the energy cost of accessing data must be minimized. This thesis presents a ferroelectric random access memory (FRAM) prototype that addresses the challenges of sensing diminishingly small charge under conditions favorable to low access energy with a time-to-digital sensing scheme. The 1 Mb IT1C FRAM fabricated in 130 nm CMOS operates from 1.5 V to 1.0 V with corresponding access energy from 19.2 pJ to 9.8 pJ per bit. Finally, the computational state of sequential elements interspersed in CMOS logic, also restricts the ability to power gate. To enable simple and fast turn-on, ferroelectric capacitors are integrated into the design of a standard cell register, whose non-volatile operation is made compatible with the digital design flow. A test-case circuit containing ferroelectric registers exhibits non-volatile operation and consumes less than 1.3 pJ per bit of state information and less than 10 clock cycles to save or restore with no minimum standby power requirement in-between active periods.by Masood Qazi.Ph.D

Data reliability and error correction for NAND Flash Memory System

NAND flash memory has been widely used for data storage due to its high density, high throughput, and low power. However, as the flash memory scales to smaller process technologies and stores more bits per cell, its reliability is decreasing. The error correction coding can be used to significantly improve the data reliability; nevertheless, the advanced ECCs such as low-density parity-check (LDPC) codes generally demand soft decisions while NAND flash memory channel provides hard-decisions only. Extracting the soft information requires the accurate characterization of flash memory channel and the effective design of coding schemes. To this end, we have presented a novel LDPC-TCM coding scheme for the Multilevel Cell (MLC) flash memories. The a posteriori TCM decoding algorithm is used in the scheme to generate soft information, which is fed to the LDPC decoder for further correction of data bits. It has been demonstrated that the proposed scheme can achieve higher error correction performance than the traditional hard-decisions based flash coding algorithms, and is feasible in the design practice. Further with the LDPC-TCM, we believe it is important to characterize the flash memory channel and investigate a method to calculate the soft decision for each bit, with the available channel outputs. We studied the various noises and interferences occurring in the memory channel and mathematically formulated the probability density function of the overall noise distribution. Based on the results we derived the final distribution for the cell threshold voltages, which can be used to instruct the calculation of soft decisions. The discoveries on the theoretical level have been demonstrated to be consistent with the real channel behaviours. The channel characterization and model provided in this dissertation can enable more design of soft-decisions based ECCs for future NAND flash memories. The data pattern processing algorithm deals with the write patterns and targets to lower the proportion of patterns that would introduce data errors. On the other hand, the voltages applied to the memory cells charges the MOSFET capacitances frequently on programming these data patterns, leading to the power problem. The high energy consumption and current spikes also cause reliability issue to the data stored in the flash memory. This dissertation proposes a write pattern formatting algorithm (WPFA) attempting to solve the two problems together. We have designed and implemented the algorithm and evaluated its performance through both the software simulations and hardware synthesis

Design and Analysis of an Adjacent Multi-bit Error Correcting Code for Nanoscale SRAMs

Increasing static random access memory (SRAM) bitcell density is a major driving force for semiconductor technology scaling. The industry standard 2x reduction in SRAM bitcell area per technology node has lead to a proliferation in memory intensive applications as greater memory system capacity can be realized per unit area. Coupled with this increasing capacity is an increasing SRAM system-level soft error rate (SER). Soft errors, caused by galactic radiation and radioactive chip packaging material corrupt a bitcell’s data-state and are a potential cause of catastrophic system failures. Further, reductions in device geometries, design rules, and sensitive node capacitances increase the probability of multiple adjacent bitcells being upset per particle strike to over 30% of the total SER below the 45 nm process node. Traditionally, these upsets have been addressed using a simple error correction code (ECC) combined with word interleaving. With continued scaling however, errors beyond this setup begin to emerge. Although more powerful ECCs exist, they come at an increased overhead in terms of area and latency. Additionally, interleaving adds complexity to the system and may not always be feasible for the given architecture. In this thesis, a new class of ECC targeted toward adjacent multi-bit upsets (MBU) is proposed and analyzed. These codes present a tradeoff between the currently popular single error correcting-double error detecting (SEC-DED) ECCs used in SRAMs (that are unable to correct MBUs), and the more robust multi-bit ECC schemes used for MBU reliability. The proposed codes are evaluated and compared against other ECCs using a custom test suite and multi-bit error channel model developed in Matlab as well as Verilog hardware description language (HDL) implementations synthesized using Synopsys Design Compiler and a commercial 65 nm bulk CMOS standard cell library. Simulation results show that for the same check-bit overhead as a conventional 64 data-bit SEC-DED code, the proposed scheme provides a corrected-SER approximately equal to the Bose-Chaudhuri- Hocquenghem (BCH) double error correcting (DEC) code, and a 4.38x improvement over the SEC-DED code in the same error channel. While, for 3 additional check-bits (still 3 less than the BCH DEC code), a triple adjacent error correcting version of the proposed code provides a 2.35x improvement in corrected-SER over the BCH DEC code for 90.9% less ECC circuit area and 17.4% less error correction delay. For further verification, a 0.4-1.0 V 75 kb single-cycle SRAM macro protected with a programmable, up-to-3-adjacent-bit-correcting version of the proposed ECC has been fab- ricated in a commercial 28 nm bulk CMOS process. The SRAM macro has undergone neu- tron irradiation testing at the TRIUMF Neutron Irradiation Facility in Vancouver, Canada. Measurements results show a 189x improvement in SER over an unprotected memory with no ECC enabled and a 5x improvement over a traditional single-error-correction (SEC) code at 0.5 V using 1-way interleaving for the same number of check-bits. This is compa- rable with the 4.38x improvement observed in simulation. Measurement results confirm an average active energy of 0.015 fJ/bit at 0.4 V, and average 80 mV reduction in VDDMIN across eight packaged chips by enabling the ECC. Both the SRAM macro and ECC circuit were designed for dynamic voltage and frequency scaling for both nominal and low voltage applications using a full-custom circuit design flow