Static Random Access Memory Research Articles

Comprehensive Study of Low-Power SRAM Design Topologies

: The need for low power in portable and smart devices is the demand to be fulfilled for sustaining the semiconductor industry. Static Random Access Memory (SRAM) is the main part of the core design in chips. It is important to reduce the leakage power consumption during the steady mode of the device for the long run of the battery. This article is about the study of different modules using pre-existing low power. Application of different methods other than lowering the supply voltage leads to an increment in the number of transistors in conventional 6T (six transistor) SRAM cells like 7T to 14T. Power gating and the Multi-threshold complementary metal oxide semiconductor (MTCMOS) technique is the most relevant method. Hybrid low power techniques are in high demand because it shows better results than using individual techniques. However, the biggest challenge is to maintain the area and delay as well. FinFET came into the scenario to overcome the leakage power and short channel effect due to scaling in CMOS. Comparative study analysis shows that FinFET decreases the overall power and delay even when the number of transistors increases. A comparison was done between 6T, 8T, and 10T using FinFET and CMOS in a paper, and concluded that FinFET shows 77.792% improved write power.

Analysis of etched drain based Cylindrical agate‐all‐around tunnel field effect transistor based static random access memory cell design

AbstractThis paper aims to propose a novel method for designing an static random access memory (SRAM) cell using an etched drain based Cyl GAA TFET with a hetero‐substrate material and an elevated density strip. The aim is to reduce power dissipation and improve stability, as demonstrated through analysis utilizing static noise margin (SNM) as well as N‐curve methods. With respect to the 16 nm MOSFET based SRAM cell, the proposed device‐based SRAM cell shows significant improvements with a 68.305% reduction in leakage power, a 15.58% increase in static voltage noise margin (SVNM), an 8.623% increase in static current noise margin (SINM), an 8.152% increase in write trip voltage (WTV), a 12.86% increase in write trip current (WTI), a 27.62% increase in static power noise margin (SPNM), and a 19.95% increase in write trip power (WTP). The design is implemented and analyzed using Cadence Virtuoso software, and a novel approach of look up tables and Verilog A is utilized for the device to circuit application. These results indicate promising advancements in the design of SRAM cells, which could have significant implications for the development of advanced computer systems.

Introducing edge intelligence to smart meters via federated split learning

The ubiquitous smart meters are expected to be a central feature of future smart grids because they enable the collection of massive amounts of fine-grained consumption data to support demand-side flexibility. However, current smart meters are not smart enough. They can only perform basic data collection and communication functions and cannot carry out on-device intelligent data analytics due to hardware constraints in terms of memory, computation, and communication capacity. Moreover, privacy concerns have hindered the utilization of data from distributed smart meters. Here, we present an end-edge-cloud federated split learning framework to enable collaborative model training on resource-constrained smart meters with the assistance of edge and cloud servers in a resource-efficient and privacy-enhancing manner. The proposed method is validated on a hardware platform to conduct building and household load forecasting on smart meters that only have 192 KB of static random-access memory (SRAM). We show that the proposed method can reduce the memory footprint by 95.5%, the training time by 94.8%, and the communication burden by 50% under the distributed learning framework and can achieve comparable or superior forecasting accuracy to that of conventional methods trained on high-capacity servers.

FinFET-Based Feedback Control Assisted Near-Threshold SRAM Cell

This paper presents a single-ended 12-transistor SRAM cell capable of operating at near-threshold voltage in FinFET technology. This cell employs Schmitt trigger-based inverters and feedback cut write assist technique to improve performance properties. The simulations have been done through HSPICE using 10 nm FinFET technology at a supply voltage of 0.45 V and a temperature of 25°C under 5000 Monte Carlo tests. The proposed cell improves the read static noise margin by at least 1.01 times. The write static noise margin is also increased by at least 1.07. The proposed single-ended cell leads to a reduction of α BL by 50% and offers low dynamic write power. The leakage power is also reduced by 1.03 times due to the use of single bitline in the cell structure, stack transistors in the write path and elimination of leakage in the read path.

A FinFET Based Low-Power Write Enhanced SRAM Cell With Improved Stability

This work introduces a FinFet based low-power 11T SRAM cell with write enhanced feature, which is considered to meet modern technology requirements due to its distinctive features and performance. The proposed 11T SRAM cell is designed in such a way, so that it reduces the overall power consumption, improving access time, and static noise margin (SNM), especially during the write operation. The proposed 11T SRAM cell is compared with other SRAM cells, including conventional 6T (conv6T), dual pull-up transistor 10T (DPUT10T), dual pull-down transistor 10T (DPDT10T), dual transmission gate 9T (DTG9T), and dual transmission gate 10T (DTG10T), at an equitable standard. The results obtained at the supply voltage of 0.5 V @ 27 °C shows the reduction in leakage power during hold 1 operation by 1.61×/1.33×/1.44×/1.63×/1.46×, and reduction in power consumption during read operation by 1.02×/1.55×/ 1.01×/1.81×/1.97× in comparison with conv6T/DPUT10T/ DPDT10T/DTG9T/DTG10T, respectively. Write access time is also improved by a factor of 1.67×/1.71×/2.59×/1.45×/1.97×, respectively. Additionally, read static noise margin (RSNM) and write static noise margin (WSNM) are improved by 2.03×/1.01×/1.65×/1.54×/1.43×, and 1.42×/1.33×/1.41×/1.02×/1.62×, respectively. This demonstrates why the proposed low-power 11T SRAM cell is desirable, especially when compared to the other cells under consideration.

Design of reliable and fast Schmitt trigger 10T SRAM cells using in-memory computing

Abstract Microprocessors, integrated data storage systems, and cache memories are just a few applications that can benefit from Static random access memory (SRAM). Computing In memory provides a solution for the von Neumann bottleneck problems. Simultaneously, it removes needless repeated data transmission between CPU and memory. Schmitt trigger was utilized for energy-efficient operation to get high reading and writing capacity. Two cross-coupled inverters with a transistor in between increase the design writing process. Furthermore, voltage boost logic is prioritized to maximize the node capacity. Using two dummy columns and dummy rows in RAM, the Replica technique controls the flow of signals via the core. The simulated results are compared to the designs that already exist. The simulation results show that the leakage power of the proposed design is 0.75 nw at 0.6 volts. The write static noise margin (WSNM) of the proposed cell is twice that of SEDF10T, which can be helpful for high-performance computing applications.

Realizing In-Memory Computing using Reliable Differential 8T SRAM for Improved Latency

Traditional von Neumann computing architectures suffer from high energy and lower speed as compared to the requirements of modern applications like those required in neural network accelerators. A modified differential eight transistor (8 + T) static random access memory (SRAM)-based in-memory computing (IMC) structure was presented for realizing bit-wise Boolean logic operations. The 8 + T SRAM-IMC is designed at the 32 nm technology node with throughput of 2.1849, 2.4815, 2.5795, 2.6240, 2.6495, 2.6619, 2.6690, 2.6732, and 2.6749 giga outputs per second for 0.5 to 1.3 V supply voltage range, respectively. The differential 8T cell used to implement logic operations supports NAND and NOR operations with minimal overhead while also performing the standard storage operation with added stability over the conventional 6T and 8T SRAM cells. The SRAM-IMC offers reliable Boolean logic operations by using asymmetric sensing strategy for all process corners, TT, SS, SF, FS, and FF for an operating temperature range of 220 K to 400 K. Monte Carlo simulation considering global threshold voltage deviation of 50 mV was performed for various operating conditions to study the impact of variations on design parameters such as latency.

Design of SRAM cell using an optimized D-latch in quantum-dot cellular automata (QCA) technology

A newer nanoscale technology called quantum-dot cellular automata (QCA) has been used by researchers to design digital circuits in place of the more traditional complementary metal–oxide semiconductor (CMOS) technology. This recent development in the technology change is due to the problems faced by CMOS technology in terms of power consumption and physical limitations. The advantages of QCA technology over CMOS technology are high density, low power consumption, high-speed operation, and less footprint area. This research provides a novel circuit for D-latch and static random access memory (SRAM) cells based on QCA technology. Initially, a D-latch circuit is proposed with a layout area of 0.01 μm2, a 0.5 clock cycle delay (latency), and a cell count of 18 QCA cells. Furthermore, an SRAM cell is proposed using the same D-latch circuit, which uses cell counts of 26 QCA cells and contributes to a layout area of 0.02 μm2 with a 0.75 clock cycle delay (latency). It is observed that our proposed circuits have a smaller layout area, fewer QCA cell counts, and a lower clock cycle delay (latency) than existing circuits.

A Better Sense Amplifier Improves the Resilience in Compute-In-Memory and Row Hammer

As machine learning (ML) workloads continue to scale, the demand for higher-density DRAM (Dynamic Random-Access Memory) and SRAM (Static Random-Access Memory) is intensifying, and also driving interest in compute-in-memory (CIM) architectures as a promising approach to enhancing computational efficiency. However, increasing DRAM cell density introduces significant challenges, particularly in the reliable identification of charge sharing between cells and bit lines. This challenge heightens the susceptibility of DRAM to row hammer attacks, where unintended data corruption occurs due to repeated access to adjacent rows. Furthermore, CIM architectures necessitate more precise sensing of bit lines to ensure accurate computation within memory. In light of these challenges, our study proposes an investigation into the potential benefits of utilizing an offset compensation sense amplifier (OCSA). The OCSA is designed to address the accuracy limitations posed by high-density DRAM in CIM applications. By enhancing the precision of bit line sensing, the OCSA may mitigate the vulnerability to row hammer attacks and improve the overall reliability and performance of CIM architectures. This study will explore the effectiveness of the OCSA in maintaining data integrity and computational accuracy within high-density DRAM environments, offering insights into its applicability for future ML workloads.

A FinFET based Low Leakage 10T Static Random Access Memory cell

A FinFET based Low Leakage 10T Static Random Access Memory cell

A 28-nm 9T SRAM-based CIM macro with capacitance weighting module and redundant array-assisted ADC

In the emerging field of Computing-in-Memory (CIM), this study introduces a 28-nm CMOS-based Static Random Access Memory (SRAM) CIM macro capable of various computational modes, potentially offering a solution to the Von Neumann bottleneck. Beyond traditional SRAM read and write operations, to enhance the flexibility of the CIM macro, a 9T cell is proposed for performing AND, OR, and XNOR operations; a new capacitive weighting module is introduced to reduce the area of conventional ladder capacitors; and a redundant array-assisted Analog-to-Digital Converter (ADC) is proposed to improve linearity during ADC quantization. The proposed architecture can achieve multi-bit multiplication and accumulation (MAC), OR accumulation (ORA), and XNOR accumulation (XNORA). Simulated using a 28-nm CMOS process, the architecture demonstrated a minor standard deviation in BL voltage of 16.27 mV at the SS process corner, as evidenced by Monte Carlo simulation. At the TT process corner, the energy expenditure for MAC, XNORA, and ORA operations was 5.76, 5.85, and 5.82 fJ/op, respectively.

Design and analysis of low power sense amplifier for static random access memory

<p>Today’s era is a digital world where each and every section of the society is experiencing and encountering with semiconductor chips. In very large-scale integration (VLSI) circuits the design of static random-access memory (SRAM) plays a crucial role in ensuring both low-power consumption and high-speed performance. The sense amplifiers (SA) are integral parts for information accessing storage in SRAM IC design. This paper introduces a dual voltage latch sense amplifier (DVLSA) for SRAM integrated circuits (IC). The comparative analyses of various SA are studied and then design a low-power SA through the implementation of energy-efficient technique. Further, we have elucidated the causes of delay and power dissipation in different SA with useful solutions and performance evaluation is conducted by comparing the proposed design with existing SA reported in the literature. The performance parameters such as power 1.604 uw, energy 470.50 fJ, delay 80.04 ps, and current 5.406 are scrutinized to assess the efficiency of the designs. The cell outcomes have been validated with cadence tool on 180 nm technology and operate at 1.8 V. The proposed design, namely, DVLSA demonstrates minimal energy consumption and low power dissipation, making it a promising advancement in SRAM IC technology.</p>

Design of dual port 9T SRAM cell with parallel processing and high performance computing

Abstract To meet industry requirements of higher transistor count SRAM cells this paper is proposing, a nine-transistor configuration static random access memory (SRAM) cell which is accessible by dual bit line and performing simultaneous read and write operations. The suggested 9T (P9T PS) cell is constructed at 32 nm CMOS and is operating at an operating voltage 0.9 V. To justify the refined performance of proposed design various analysis are carried out that shows the static noise margins (SNM) that occurs while executing the read operation is 0.368 V and hold operation by the cell is 0.364 V, while the write operation has an SNM of 0.422 V. The access time for write and read functions of the cell is found to be 1.2 nS and 4.5 nS respectively. The P9T PS SRAM is compared with other dual-port and single-port 9T SRAM cells is stand out to be pre-eminence in performance in terms of noise immunity, temperature variations, power consumption (static and dynamic both), power delay product (PDP), SNM to PDP and area ratio (SAPR) and electric quality matric. Total leakage power for P9T PS cell is found to be 22.1 nW. The P9T PS bit cell is also found to be power efficient as it is consuming minimal power for all of the operations. Area layout of the P9T (PS) is found to be 22.372 μm2. The results are also supported by the process corner variation analysis for four different corners. LT-spice software is used to carry out all the simulations and analysis.

Efficient turbo product code decoder with Build‐In SRAM‐based transpose memory

AbstractTurbo product codes (TPCs) have been widely used for bit error correction in high‐speed applications such as data storage. This letter introduces an efficient hard‐input hard‐output iterating TPC decoder module. A transpose memory utilizing static random access memory (SRAM) is integrated into the decoder to achieve a low hardware overhead. The transpose memory, based on an 8T SRAM bit‐cell, supports both horizontal (row‐wise), and vertical (column‐wise) read/write operations. It is prototyped under a 28nm high‐k/metal gate stack process with bit‐cell size of 0.582 µm2. This specialized SRAM significantly reduces the hardware overhead when compared with a register‐array‐based transpose memory without significant throughput loss. A field programmable gate array (FPGA) evaluation platform is utilized to emulate the TPC decoder module, and the maximum decoder throughput is up to 6.49 Gbps at 250 MHz.

Synergistic Effects of Total Ionizing Dose and Single-Event Upset in 130 nm 7T Silicon-on-Insulator Static Random Access Memory

The exposure of spaceborne devices to high-energy charged particles in space results in the occurrence of both a total ionizing dose (TID) and the single-event effect (SEE). These phenomena present significant challenges for the reliable operation of spacecraft and satellites. The rapid advancement of semiconductor fabrication processes and the continuous reduction in device feature size have led to an increase in the significance of the synergistic effects of TID and SEE in static random access memory (SRAM). In order to elucidate the involved physical mechanisms, the synergistic effects of TID and single-event upset (SEU) in a new kind of 130 nm 7T silicon-on-insulator (SOI) SRAM were investigated by means of cobalt-60 gamma-ray and heavy ion irradiation experiments. The findings demonstrate that 7T SOI SRAM is capable of maintaining normal reading and writing functionality when subjected to TID irradiation at a total dose of up to 750 krad(Si). In general, the TID was observed to reduce the SEU cross-section of the 7T SOI SRAM. However, the extent of this reduction was influenced by the heavy ion LET value and the specific writing data pattern employed. Based on the available evidence, it can be proposed that TID preirradiation represents a promising avenue for enhancing the resilience of 7T SOI SRAMs to SEU.

Analyzing Aging Effects on SRAM PUFs: Implications for Security and Reliability

The aging effects on Static Random-Access Memory Physical Unclonable Functions (SRAM PUFs) pose significant security and reliability challenges for current hardware systems. SRAM PUFs utilize process variations in integrated circuits for secure key generation and device authentication. However, aging effects such as bias temperature instability (BTI) and hot carrier injection (HCI) may change SRAM cell characteristics and affect PUF responses. This study addresses various aging-induced variation challenges, identifies security vulnerabilities, and provides innovative approaches to risk reduction. Furthermore, this study focuses on concerns about SRAM PUF reliability. In this regard, mitigation approaches like adaptive reconfiguration, error correction codes, and multi-modal PUFs are introduced to enhance the resilience of SRAM PUFs. This study concludes by outlining future research directions and prospects for improving SRAM PUF-based security solutions by several orders of magnitude. This is carried out with various complexities related to semiconductor device aging.

Design analysis of a low-power, high-speed 8 T SRAM cell using dual-threshold CNTFETs

Recently, carbon nanotube field-effect transistors (CNTFETs) have garnered significant attention from VLSI engineers due to their exceptional electrical properties. This paper proposes a novel high-speed, low-power eight-transistor (8 T) static random-access memory (SRAM) cell based on 32-nm CNTFET technology. The SRAM cell was simulated using the HSPICE tool with a VDD of 0.9 V. The high-speed and low-power characteristics of the SRAM design are attributed to the high subthreshold slope and high carrier mobility of metal-oxide-semiconductor field-effect transistor (MOSFET)-like CNTFETs utilized in the simulations. The implementation of dual threshold transistors, coupled with a transmission gate for bitline access, contributes to the enhanced performance. Key performance metrics such as noise margins, power consumption, delay, and SRAM electrical quality metric (SEQM) of the proposed SRAM have been evaluated and compared with existing CNTFET-based SRAM designs. The proposed cell demonstrates reductions of 73.73%, 43.18%, and 58.70% in read power, write power, and hold power, respectively, compared to the lowest respective power values of other examined SRAM designs. The proposed SRAM ranks second, third, and second in write static noise margin (WSNM), hold static noise margin (HSNM), and read static noise margin (RSNM), respectively, among other designs. Additionally, the proposed SRAM exhibits the least sensitivity to parametric variations compared to other designs. The SEQM, which provides a comprehensive assessment of access times, noise margins, and power usage for the SRAM cell, has been calculated. The SEQM of the proposed SRAM is 10.6, 1.89, 13.15, and 1.82 times higher than that of C6T, BLP8T, Mani’s 10 T, and LP8T, respectively.

A novel MTCMOS based 8T2M NVSRAM design for low power applications with high temperature endurance

This research investigates, for the first time, a novel eight-transistor-two-memristor (8T2M) nonvolatile static random access memory (NVSRAM) with 7-nm technology. The key innovation in this design lies in the incorporation of multiple-threshold complementary metal oxide semiconductor (MTCMOS) technology with power gating technique, which enables efficient power management and enhanced performance with low leakage current. The implementation of multiple threshold voltage levels allows for dynamic control of transistor behavior, optimizing power consumption and read/write speeds. As compared to a traditional six-transistor (6T) static random access memory (SRAM) cell, it has been ascertained that there is a 33% enhancement in the read margin and an 18% improvement in the write margin. Moreover, the delay for read, write ‘0’ and write ‘1’ is also minimized by 63.89%, 37.99% and 42.77%. Furthermore, the power attenuation is also reduced for read and write by 63.02% and 81.6%, respectively with respect to a conventional SRAM.

Gate breakdown induced stuck bits in sub-20 nm FinFET SRAM

This paper discusses the stuck bits induced by the gate breakdown of PMOS in the sub-20 nm FinFET static random access memory (SRAM) device. After the heavy-ion experiment, several stuck bits are in the FinFET SRAM matrix, which cannot be set or reset. To investigate the factors that caused the stuck bit, we perform the electrical failure analysis (EFA) to measure the electric characteristics of the transistors in the stuck bits and normal cells separately. The measurement results show a clear gate breakdown at the PMOS pull-up (PPU) transistor in all of the measured SRAM cells. Meanwhile, the SPICE simulation based on the EFA results verifies the impact of the damaged PPU. Finally, considering all of the phenomena, the charge deposition from single events is the major mechanism that causes the stuck bits in FinFET SRAM. The experiment results alert a potential gate breakdown risk in the ultra-high-density FinFET SRAM applied in the space environment.

Radiation Tolerant by Design 12-transistor Static Random Access Memory

Radiation Tolerant by Design 12-transistor Static Random Access Memory