Design of Low-Voltage Digital Building Blocks and ADCs for Energy-Efficient Systems

Increasing number of energy-limited applications continue to drive the demand for designing systems with high energy efficiency. This tutorial covers the main building blocks of a system implementation including digital logic, embedded memories, and analog-to-digital converters and describes the challenges and solutions to designing these blocks for low-voltage operation

A Process Variation Tolerant Self-Compensation Sense Amplifier Design

As we move under the aegis of the Moore\u27s law, we have to deal with its darker side with problems like leakage and short channel effects. Once we go beyond 45nm regime process variations also have emerged as a significant design concern.Embedded memories uses sense amplifier for fast sensing and typically, sense amplifiers uses pair of matched transistors in a positive feedback environment. A small difference in voltage level of applied input signals to these matched transistors is amplified and the resulting logic signals are latched. Intra die variation causes mismatch between the sense transistors that should ideally be identical structures. Yield loss due to device and process variations has never been so critical to cause failure in circuits. Due to growth in size of embedded SRAMs as well as usage of sense amplifier based signaling techniques, process variations in sense amplifiers leads to significant loss of yield for that we need to come up with process variation tolerant circuit styles and new devices. In this work impact of transistor mismatch due to process variations on sense amplifier is evaluated and this problem is stated. For the solution of the problem a novel self compensation scheme on sense amplifiers is presented on different technology nodes up to 32nm on conventional bulk MOSFET technology. Our results show that the self compensation technique in the conventional bulk MOSFET latch type sense amplifier not just gives improvement in the yield but also leads to improvement in performance for latch type sense amplifiers. Lithography related CD variations, fluctuations in dopant density, oxide thickness and parametric variations of devices are identified as a major challenge to the classical bulk type MOSFET. With the emerging nanoscale devices, SIA roadmap identifies FinFETs as a candidate for post-planar end-of-roadmap CMOS device. With current technology scaling issues and with conventional bulk type MOSFET on 32nm node our technique can easily be applied to Double Gate devices. In this work, we also develop the model of Double Gate MOSFET through 3D Device Simulator Damocles and TCAD simulator. We propose a FinFET based process variation tolerant sense amplifier design that exploits the back gate of FinFET devices for dynamic compensation against process variations. Results from statistical simulation show that the proposed dynamic compensation is highly effective in restoring yield at a level comparable to that of sense amplifiers without process variations. We created the 32nm double gate models generated from Damocles 3-D device simulations [25] and Taurus Device Simulator available commercially from Synopsys [47] and use them in the nominal latch type sense amplifier design and on the Independent Gate Self Compensation Sense Amplifier Design (IGSSA) to compare the yield and performance benefits of sense amplifier design on FinFET technology over the conventional bulk type CMOS based sense amplifier on 32nm technology node effective in restoring yield at a level comparable to that of sense amplifiers without process variations. We created the 32nm double gate models generated from Damocles 3-D device simulations [25] and Taurus Device Simulator available commercially from Synopsys [47] and use them in the nominal latch type sense amplifier design and on the Independent Gate Self Compensation Sense Amplifier Design (IGSSA) to compare the yield and performance benefits of sense amplifier design on FinFET technology over the conventional bulk type CMOS based sense amplifier on 32nm technology node

Ultra-Low-Power Embedded SRAM Design for Battery- Operated and Energy-Harvested IoT Applications

Internet of Things (IoT) devices such as wearable health monitors, augmented reality goggles, home automation, smart appliances, etc. are a trending topic of research. Various IoT products are thriving in the current electronics market. The IoT application needs such as portability, form factor, weight, etc. dictate the features of such devices. Small, portable, and lightweight IoT devices limit the usage of the primary energy source to a smaller rechargeable or non-rechargeable battery. As battery life and replacement time are critical issues in battery-operated or partially energy-harvested IoT devices, ultra-low-power (ULP) system on chips (SoC) are becoming a widespread solution of chip makers’ choice. Such ULP SoC requires both logic and the embedded static random access memory (SRAM) in the processor to operate at very low supply voltages. With technology scaling for bulk and FinFET devices, logic has demonstrated to operate at low minimum operating voltages (VMIN). However, due to process and temperature variation, SRAMs have higher VMIN in scaled processes that become a huge problem in designing ULP SoC cores. This chapter discusses the latest published circuits and architecture techniques to minimize the SRAM VMIN for scaled bulk and FinFET technologies and improve battery life for ULP IoT applications

SRAM Read-Assist Scheme for Low Power High Performance Applications

Semiconductor technology scaling resulted in a considerable reduction in the transistor cost and an astonishing enhancement in the performance of VLSI (very large scale integration) systems. These nanoscale technologies have facilitated integration of large SRAMs which are now very popular for both processors and system-on-chip (SOC) designs. The density of SRAM array had a quadratic increase with each generation of CMOS technology. However, these nanoscale technologies unveiled few significant challenges to the design of high performance and low power embedded memories. First, process variation has become more significant in these technologies which threaten reliability of sensing circuitry. In order to alleviate this problem, we need to have larger signal swings on the bitlines (BLs) which degrade speed as well as power dissipation. The second challenge is due to the variation in the cell current which will reduce the worst case cell current. Since this cell current is responsible for discharging BLs, this problem will translate to longer activation time for the wordlines (WLs). The longer the WL pulse width is, the more likely is the cell to be unstable. A long WL pulse width can also degrade noise margin. Furthermore, as a result of continuous increase in the size of SRAMs, the BL capacitance has increased significantly which will deteriorate speed as well as power dissipation. The aforementioned problems require additional techniques and treatment such as read-assist techniques to insure fast, low power and reliable read operation in nanoscaled SRAMs. In this research we address these concerns and propose a read-assist sense amplifier (SA) in 65nm CMOS technology that expedites the process of developing differential voltage to be sensed by sense amplifier while reducing voltage swing on the BLs which will result in increased sensing speed, lower power and shorter WL activation time. A complete comparison is made between the proposed scheme, conventional SA and a state of the art design which shows speed improvement and power reduction of 56.1% and 25.9%, respectively over the conventional scheme at the expense of negligible area overhead. Also, the proposed scheme enables us to reduce cell VDD for having the same sensing speed which results in considerable reduction in leakage power dissipation

Ultra-low-power SRAM design in high variability advanced CMOS

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 163-181).Embedded SRAMs are a critical component in modern digital systems, and their role is preferentially increasing. As a result, SRAMs strongly impact the overall power, performance, and area, and, in order to manage these severely constrained trade-offs, they must be specially designed for target applications. Highly energy-constrained systems (e.g. implantable biomedical devices, multimedia handsets, etc.) are an important class of applications driving ultra-low-power SRAMs. This thesis analyzes the energy of an SRAM sub-array. Since supply- and threshold-voltage have a strong effect, targets for these are established in order to optimize energy. Despite the heavy emphasis on leakage-energy, analysis of a high-density 256x256 sub-array in 45nm LP CMOS points to two necessary optimizations: (1) aggressive supply-voltage reduction (in addition to Vt elevation), and (2) performance enhancement. Important SRAM metrics, including read/write/hold-margin and read-current, are also investigated to identify trade-offs of these optimizations. Based on the need to lower supply-voltage, a 0.35V 256kb SRAM is demonstrated in 65nm LP CMOS. It uses an 8T bit-cell with peripheral circuit-assists to improve write-margin and bit-line leakage. Additionally, redundancy, to manage the increasing impact of variability in the periphery, is proposed to improve the area-offset trade-off of sense-amplifiers, demonstrating promise for highly advanced technology nodes. Based on the need to improve performance, which is limited by density constraints, a 64kb SRAM, using an offset-compensating sense-amplifier, is demonstrated in 45nm LP CMOS with high-density 0.25[mu]m2 bit-cells.(cont.) The sense-amplifier is regenerative, but non -strobed, overcoming timing uncertainties limiting performance, and it is single-ended, for compatibility with 8T cells. Compared to a conventional strobed sense-amplifier, it achieves 34% improvement in worst-case access-time and 4x improvement in the standard deviation of the access-time.by Naveen Verma.Ph.D

Modeling and Mitigation of Soft Errors in Nanoscale SRAMs

Energetic particle (alpha particle, cosmic neutron, etc.) induced single event data upset or soft error has emerged as a key reliability concern in SRAMs in sub-100 nanometre technologies. Low operating voltage, small node capacitance, high packing density, and lack of error masking mechanisms are primarily responsible for the soft error susceptibility of SRAMs. In addition, since SRAM occupies the majority of die area in system-on-chips (SoCs) and microprocessors, different leakage reduction techniques, such as, supply voltage reduction, gated grounding, etc., are applied to SRAMs in order to limit the overall chip leakage. These leakage reduction techniques exponentially increase the soft error rate in SRAMs. The soft error rate is further accentuated by process variations, which are prominent in scaled-down technologies. In this research, we address these concerns and propose techniques to characterize and mitigate soft errors in nanoscale SRAMs. We develop a comprehensive analytical model of the critical charge, which is a key to assessing the soft error susceptibility of SRAMs. The model is based on the dynamic behaviour of the cell and a simple decoupling technique for the non-linearly coupled storage nodes. The model describes the critical charge in terms of NMOS and PMOS transistor parameters, cell supply voltage, and noise current parameters. Consequently, it enables characterizing the spread of critical charge due to process induced variations in these parameters and to manufacturing defects, such as, resistive contacts or vias. In addition, the model can estimate the improvement in critical charge when MIM capacitors are added to the cell in order to improve the soft error robustness. The model is validated by SPICE simulations (90nm CMOS) and radiation test. The critical charge calculated by the model is in good agreement with SPICE simulations with a maximum discrepancy of less than 5%. The soft error rate estimated by the model for low voltage (sub 0.8 V) operations is within 10% of the soft error rate measured in the radiation test. Therefore, the model can serve as a reliable alternative to time consuming SPICE simulations for optimizing the critical charge and hence the soft error rate at the design stage. In order to limit the soft error rate further, we propose an area-efficient multiword based error correction code (MECC) scheme. The MECC scheme combines four 32 bit data words to form a composite 128 bit ECC word and uses an optimized 4-input transmission-gate XOR logic. Thus MECC significantly reduces the area overhead for check-bit storage and the delay penalty for error correction. In addition, MECC interleaves two composite words in a row for limiting cosmic neutron induced multi-bit errors. The ground potentials of the composite words are controlled to minimize leakage power without compromising the read data stability. However, use of composite words involves a unique write operation where one data word is written while other three data words are read to update the check-bits. A power efficient word line signaling technique is developed to facilitate the write operation. A 64 kb SRAM macro with MECC is designed and fabricated in a commercial 90nm CMOS technology. Measurement results show that the SRAM consumes 534 μW at 100 MHz with a data latency of 3.3 ns for a single bit error correction. This translates into 82% per-bit energy saving and 8x speed improvement over recently reported multiword ECC schemes. Accelerated neutron radiation test carried out at TRIUMF in Vancouver confirms that the proposed MECC scheme can correct up to 85% of soft errors

X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories

Silicon-based Static Random Access Memories (SRAM) and digital Boolean logic have been the workhorse of the state-of-art computing platforms. Despite tremendous strides in scaling the ubiquitous metal-oxide-semiconductor transistor, the underlying \textit{von-Neumann} computing architecture has remained unchanged. The limited throughput and energy-efficiency of the state-of-art computing systems, to a large extent, results from the well-known \textit{von-Neumann bottleneck}. The energy and throughput inefficiency of the von-Neumann machines have been accentuated in recent times due to the present emphasis on data-intensive applications like artificial intelligence, machine learning \textit{etc}. A possible approach towards mitigating the overhead associated with the von-Neumann bottleneck is to enable \textit{in-memory} Boolean computations. In this manuscript, we present an augmented version of the conventional SRAM bit-cells, called \textit{the X-SRAM}, with the ability to perform in-memory, vector Boolean computations, in addition to the usual memory storage operations. We propose at least six different schemes for enabling in-memory vector computations including NAND, NOR, IMP (implication), XOR logic gates with respect to different bit-cell topologies $-$ the 8T cell and the 8$^+$T Differential cell. In addition, we also present a novel \textit{`read-compute-store'} scheme, wherein the computed Boolean function can be directly stored in the memory without the need of latching the data and carrying out a subsequent write operation. The feasibility of the proposed schemes has been verified using predictive transistor models and Monte-Carlo variation analysis.Comment: This article has been accepted in a future issue of IEEE Transactions on Circuits and Systems-I: Regular Paper

Low-Power Soft-Error-Robust Embedded SRAM

Soft errors are radiation-induced ionization events (induced by energetic particles like alpha particles, cosmic neutron, etc.) that cause transient errors in integrated circuits. The circuit can always recover from such errors as the underlying semiconductor material is not damaged and hence, they are called soft errors. In nanometer technologies, the reduced node capacitance and supply voltage coupled with high packing density and lack of masking mechanisms are primarily responsible for the increased susceptibility of SRAMs towards soft errors. Coupled with these are the process variations (effective length, width, and threshold voltage), which are prominent in scaled-down technologies. Typically, SRAM constitutes up to 90% of the die in microprocessors and SoCs (System-on-Chip). Hence, the soft errors in SRAMs pose a potential threat to the reliable operation of the system. In this work, a soft-error-robust eight-transistor SRAM cell (8T) is proposed to establish a balance between low power consumption and soft error robustness. Using metrics like access time, leakage power, and sensitivity to single event transients (SET), the proposed approach is evaluated. For the purpose of analysis and comparisons the results of 8T cell are compared with a standard 6T SRAM cell and the state-of-the-art soft-error-robust SRAM cells. Based on simulation results in a 65-nm commercial CMOS process, the 8T cell demonstrates higher immunity to SETs along with smaller area and comparable leakage power. A 32-kb array of 8T cells was fabricated in silicon. After functional verification of the test chip, a radiation test was conducted to evaluate the soft error robustness. As SRAM cells are scaled aggressively to increase the overall packing density, the smaller transistors exhibit higher degrees of process variation and mismatch, leading to larger offset voltages. For SRAM sense amplifiers, higher offset voltages lead to an increased likelihood of an incorrect decision. To address this issue, a sense amplifier capable of cancelling the input offset voltage is presented. The simulated and measured results in 180-nm technology show that the sense amplifier is capable of detecting a 4 mV differential input signal under dc and transient conditions. The proposed sense amplifier, when compared with a conventional sense amplifier, has a similar die area and a greatly reduced offset voltage. Additionally, a dual-input sense amplifier architecture is proposed with corroborating silicon results to show that it requires smaller differential input to evaluate correctly.1 yea