Design and Analysis of Robust Low Voltage Static Random Access Memories.

Static Random Access Memory (SRAM) is an indispensable part of most modern VLSI designs and dominates silicon area in many applications. In scaled technologies, maintaining high SRAM yield becomes more challenging since they are particularly vulnerable to process variations due to 1) the minimum sized devices used in SRAM bitcells and 2) the large array sizes. At the same time, low power design is a key focus throughout the semiconductor industry. Since low voltage operation is one of the most effective ways to reduce power consumption due to its quadratic relationship to energy savings, lowering the minimum operating voltage (Vmin) of SRAM has gained significant interest. This thesis presents four different approaches to design and analyze robust low voltage SRAM: SRAM analysis method improvement, SRAM bitcell development, SRAM peripheral optimization, and advance device selection. We first describe a novel yield estimation method for bit-interleaved voltage-scaled 8-T SRAMs. Instead of the traditional trade-off between write and read, the trade-off between write and half select disturb is analyzed. In addition, this analysis proposes a method to find an appropriate Write Word-Line (WWL) pulse width to maximize yield. Second, low leakage 10-T SRAM with speed compensation scheme is proposed. During sleep mode of a sensor application, SRAM retaining data cannot be shut down so it is important to minimize leakage in SRAM. This work adopts several leakage reduction techniques while compensating performance. Third, adaptive write architecture for low voltage 8-T SRAMs is proposed. By adaptively modulating WWL width and voltage level, it is possible to achieve low power consumption while maintaining high yield without excessive performance degradation. Finally, low power circuit design based on heterojunction tunneling transistors (HETTs) is discussed. HETTs have a steep subthreshold swing beneficial for low voltage operation. Device modeling and design of logic and SRAM are proposed.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91569/1/daeyeonk_1.pd

Low-power and application-specific SRAM design for energy-efficient motion estimation

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. 181-189).Video content is expected to account for 70% of total mobile data traffic in 2015. High efficiency video coding, in this context, is crucial for lowering the transmission and storage costs for portable electronics. However, modern video coding standards impose a large hardware complexity. Hence, energy-efficiency of these hardware blocks is becoming more critical than ever before for mobile devices. SRAMs are critical components in almost all SoCs affecting the overall energy-efficiency. This thesis focuses on algorithm and architecture development as well as low-power and application-specific SRAM design targeting motion estimation. First, a motion estimation design is considered for the next generation video standard, HEVC. Hardware cost and coding efficiency trade-offs are quantified and an optimum design choice between hardware complexity and coding efficiency is proposed. Hardware-efficient search algorithm, shared search range across CU engines and pixel pre-fetching algorithms provide 4.3x area, 56x on-chip bandwidth and 151 x off-chip bandwidth reduction. Second, a highly-parallel motion estimation design targeting ultra-low voltage operation and supporting AVC/H.264 and VC-1 standards are considered. Hardware reconfigurability along with frame and macro-block parallel processing are implemented for this engine to maximize hardware sharing between multiple standards and to meet throughput constraints. Third, in the context of low-power SRAMs, a 6T and an 8T SRAM are designed in 28nm and 45nm CMOS technologies targeting low voltage operation. The 6T design achieves operation down to 0.6V and the 8T design achieves operation down to 0.5V providing ~ 2.8x and ~ 4.8x reduction in energy/access respectively. Finally, an application-specific SRAM design targeted for motion estimation is developed. Utilizing the correlation of pixel data to reduce bit-line switching activity, this SRAM achieves up to 1.9x energy savings compared to a similar conventional 8T design. These savings demonstrate that application-specific SRAM design can introduce a new dimension and can be combined with voltage scaling to maximize energy-efficiency.by Mahmut Ersin Sinangil.Ph.D

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

Design and failure analysis of logic-compatible multilevel gain-cell-based DRAM for fault-tolerant VLSI systems

This paper considers the problem of increasing the storage density in fault-tolerant VLSI systems which require only limited data retention times. To this end, the concept of storing many bits per memory cell is applied to area-efficient and fully logic-compatible gain-cell-based dynamic memories. A memory macro in 90-nm CMOS technology including multilevel write and read circuits is proposed and analyzed with respect to its read failure probability due to within-die process variations by means of Monte Carlo simulations

Mr.Wolf: An Energy-Precision Scalable Parallel Ultra Low Power SoC for IoT Edge Processing

This paper presents Mr. Wolf, a parallel ultra-low power (PULP) system on chip (SoC) featuring a hierarchical architecture with a small (12 kgates) microcontroller (MCU) class RISC-V core augmented with an autonomous IO subsystem for efficient data transfer from a wide set of peripherals. The small core can offload compute-intensive kernels to an eight-core floating-point capable of processing engine available on demand. The proposed SoC, implemented in a 40-nm LP CMOS technology, features a 108-mu W fully retentive memory (512 kB). The IO subsystem is capable of transferring up to 1.6 Gbit/s from external devices to the memory in less than 2.5 mW. The eight-core compute cluster achieves a peak performance of 850 million of 32-bit integer multiply and accumulate per second (MMAC/s) and 500 million of 32-bit floating-point multiply and accumulate per second (MFMAC/s) -1 GFlop/s-with an energy efficiency up to 15 MMAC/s/mW and 9 MFMAC/s/mW. These building blocks are supported by aggressive on-chip power conversion and management, enabling energy-proportional heterogeneous computing for always-on IoT end nodes improving performance by several orders of magnitude with respect to traditional single-core MCUs within a power envelope of 153 mW. We demonstrated the capabilities of the proposed SoC on a wide set of near-sensor processing kernels showing that Mr. Wolf can deliver performance up to 16.4 GOp/s with energy efficiency up to 274 MOp/s/mW on real-life applications, paving the way for always-on data analytics on high-bandwidth sensors at the edge of the Internet of Things

Solid State Circuits Technologies

The evolution of solid-state circuit technology has a long history within a relatively short period of time. This technology has lead to the modern information society that connects us and tools, a large market, and many types of products and applications. The solid-state circuit technology continuously evolves via breakthroughs and improvements every year. This book is devoted to review and present novel approaches for some of the main issues involved in this exciting and vigorous technology. The book is composed of 22 chapters, written by authors coming from 30 different institutions located in 12 different countries throughout the Americas, Asia and Europe. Thus, reflecting the wide international contribution to the book. The broad range of subjects presented in the book offers a general overview of the main issues in modern solid-state circuit technology. Furthermore, the book offers an in depth analysis on specific subjects for specialists. We believe the book is of great scientific and educational value for many readers. I am profoundly indebted to the support provided by all of those involved in the work. First and foremost I would like to acknowledge and thank the authors who worked hard and generously agreed to share their results and knowledge. Second I would like to express my gratitude to the Intech team that invited me to edit the book and give me their full support and a fruitful experience while working together to combine this book

Design of robust spin-transfer torque magnetic random access memories for ultralow power high performance on-chip cache applications

Spin-transfer torque magnetic random access memories (STT-MRAMs) based on magnetic tunnel junction (MTJ) has become the leading candidate for future universal memory technology due to its potential for low power, non-volatile, high speed and extremely good endurance. However, conflicting read and write requirements exist in STT-MRAM technology because the current path during read and write operations are the same. Read and write failures of STT-MRAMs are degraded further under process variations. The focus of this dissertation is to optimize the yield of STT- MRAMs under process variations by employing device-circuit-architecture co-design techniques. A devices-to-systems simulation framework was developed to evaluate the effectiveness of the techniques proposed in this dissertation. An optimization methodology for minimizing the failure probability of 1T-1MTJ STT-MRAM bit-cell by proper selection of bit-cell configuration and access transistor sizing is also proposed. A failure mitigation technique using assistsin 1T-1MTJ STT-MRAM bit-cells is also proposed and discussed. Assist techniques proposed in this dissertation to mitigate write failures either increase the amount of current available to switch the MTJ during write or decrease the required current to switch the MTJ. These techniques achieve significant reduction in bit-cell area and write power with minimal impact on bit-cell failure probability and read power. However, the proposed write assist techniques may be less effective in scaled STT-MRAM bit-cells. Furthermore, read failures need to be overcome and hence, read assist techniques are required. It has been experimentally demonstrated that a class of materials called multiferroics can enable manipulation of magnetization using electric fields via magnetoelectric effects. A read assist technique using an MTJ structure incorporating multiferroic materials is proposed and analyzed. It was found that it is very difficult to overcome the fundamental design issues with 1T-1MTJ STT-MRAM due to the two-terminal nature of the MTJ. Hence, multi-terminal MTJ structures consisting of complementary polarized pinned layers are proposed. Analysis of the proposed MTJ structures shows significant improvement in bit-cell failures. Finally, this dissertation explores two system-level applications enabled by STT-MRAMs, and shows that device-circuit-architecture co-design of STT-MRAMs is required to fully exploit its benefits

Interconnect and Memory Design for Intelligent Mobile System

Technology scaling has driven the transistor to a smaller area, higher performance and lower power consuming which leads us into the mobile and edge computing era. However, the benefits of technology scaling are diminishing today, as the wire delay and energy scales far behind that of the logics, which makes communication more expensive than computation. Moreover, emerging data centric algorithms like deep learning have a growing demand on SRAM capacity and bandwidth. High access energy and huge leakage of the large on-chip SRAM have become the main limiter of realizing an energy efficient low power smart sensor platform. This thesis presents several architecture and circuit solutions to enable intelligent mobile systems, including voltage scalable interconnect scheme, Compute-In-Memory (CIM), low power memory system from edge deep learning processor and an ultra-low leakage stacked voltage domain SRAM for low power smart image signal processor (ISP). Four prototypes are implemented for demonstration and verification. The first two seek the solutions to the slow and high energy global on-chip interconnect: the first prototype proposes a reconfigurable self-timed regenerator based global interconnect scheme to achieve higher performance and energy-efficiency in wide voltage range, while the second one presents a non Von Neumann architecture, a hybrid in-/near-memory Compute SRAM (CRAM), to address the locality issue. The next two works focus on low-power low-leakage SRAM design for Intelligent sensors. The third prototype is a low power memory design for a deep learning processor with 270KB custom SRAM and Non-Uniform Memory Access architecture. The fourth prototype is an ultra-low leakage SRAM for motion-triggered low power smart imager sensor system with voltage domain stacking and a novel array swapping mechanism. The work presented in this dissertation exploits various optimizations in both architecture level (exploiting temporal and spatial locality) and circuit customization to overcome the main challenges in making extremely energy-efficient battery-powered intelligent mobile devices. The impact of the work is significant in the era of Internet-of-Things (IoT) and the age of AI when the mobile computing systems get ubiquitous, intelligent and longer battery life, powered by these proposed solutions.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155232/1/jiwang_1.pd