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This paper presents experimental results of nMOS quantum dot gate field effect transistor (QDGFET) based inverter devices for SRAM devices. A three-state inverter device was fabricated and tested with Si/SiO₂ quantum dots. The work performed here builds off previous works performed with Si/SiO₂ dot-based inverters which used two layers of quantum dots. This research explores multi-state SRAM device operation. A three-state (Si QDs) and a four-state (Si and Ge QDs) inverter are described, and they will allow for multistate logic devices to be utilized in everyday logic chips, which will require less devices to perform the same tasks as conventional devices, double the capacity of the device, and require less power, which will generate less of a thermal footprint. The data of the Half Cell SRAM, comprised of one access transistor and an inverter along with a capacitor, is presented here.

Flexible resistive switching memory devices based on graphene oxide (GO) polymer nanocomposite were prepared on flexible substrate to research the influence of bending on resistive switching behavior. The devices showed evident response in resistive switching memory characteristics to flexible bending. The 2000 cycles flexible bending leads to the switch of resistive switching memory characteristic from write-once-read-many time memory (WORM) to static random access memory (SRAM). Both WORM and SRAM memory properties are all repeatable, and the threshold switching voltage also showed good consistency. The resistive switching mechanism is attributed to the formation of carbon-rich conductive filaments for nonvolatile WORM characteristics. The bending-induced micro-crack may be responsible for the partial broken of the electrical channels, and may lead to the volatile SRAM characteristics.

A novel 7T carbon nanotube field effect transistor (CNTFET)-based static random-access memory (SRAM) cell is proposed in this paper. Power and noise margin performances of the proposed SRAM cell is observed for write, hold and read operations. The power consumption and noise margin of the proposed SRAM cell is compared with the conventional 6T and 8T CNTFET-based SRAM cells. From the simulation, it is noted that the proposed 7T SRAM cell consumes lesser power and offers high static noise margin (SNM) compared to that of conventional 6T and 8T SRAM cells. The introduction of diode-based transistor structure improves the power and noise performance of the proposed SRAM cell. The effect of variation of parameters such as gate oxide thickness, dielectric constant, pitch, temperature, number of carbon nanotubes (CNT) and supply voltage on power and noise performance of proposed 7T SRAM cell is studied. Simulations were carried out with HSPICE simulation tool using Stanford University 32-nm CNTFET model.

This paper describes fabrication of Quantum Dot Gate n-FETs using SiOx-cladded Si quantum dot self-assembled on the tunnel gate oxide. Experimental I-V characteristics exhibiting 4-states are presented. Simulation is presented for the operation of viable 4-state SRAMs using QDG-FETs.

This research paper proposes a low-power, high-stability 8T static random access memory (SRAM) cell. The proposed SRAM cell is a modified structure of the conventional 6T SRAM cell. The introduction of two diode-connected transistors in the pull-down network of the conventional 6T SRAM cell gives the proposed 8T SRAM cell structure. The presence of diode-connected transistors improves the power and noise performances of the proposed cell as compared to those of the conventional bit cells. The power consumption and static noise margin (SNM) of the suggested SRAM cell are calculated for write, hold and read operations. Also, the write and read delays of the proposed and conventional bit cells are observed. The power, speed, noise margin and area of the proposed 8T SRAM cell are compared with those of some of the existing SRAM cells. In comparison to conventional SRAM cells, the proposed cell consumes less power and has higher stability, according to the study. A novel dual-supply ($\mathrm{\text{VDD}}+=1.4$V and $\mathrm{\text{VSS}}-=-0.2$V or 200mV) concept is applied for the existing and proposed SRAM cells. The noise and power performances of SRAM cells are well improved under the condition of dual supply as compared to the conventional supply voltage ($\mathrm{\text{VDD}}=1.2$V and $\mathrm{\text{VSS}}=0$V). A $4\times 4$ memory array of the proposed 8T SRAM cell is formed and the performance of the array structure is compared with that of $4\times 4$ array of the conventional 6T SRAM cell. The layouts of existing and proposed SRAM cells are illustrated. The simulation is carried out using the Cadence Virtuoso simulation EDA tool in 90-nm CMOS technology.

Many researchers are actively working on developing a fast-performing static random-access memory (SRAM) cell with low-power consumption and high stability. This study also introduces one such new and all-round excellent SRAM cell. In this paper, an SRAM cell with eleven transistors (11T) developed using carbon nanotube field effect transistor (CNTFET) is introduced. This new 11T CNTFET SRAM cell is another variant of the Schmitt-trigger (ST)-based SRAM cell. This new SRAM cell structure is achieved by incorporating a single-ended write mode, a feed-back cutting technique and a single-ended read approach into a Schmitt-trigger (ST)-based SRAM cell. The WSNM of the proposed 11T CNTFET SRAM cell is increased by using single-ended writing scheme and feed-back cutting method in the cell. The single ended read approach of 11T CNTFET SRAM cell increases the RSNM as the storage nodes are not disturbed. The write power, hold power, read power, WSNM, HSNM, RSNM, write delay and read delay of this 11T CNTFET SRAM cell are 2.1538e-10 W, 1.7077e-09 W, 1.4524e-08 W, 423.61 mV, 402.20 mV, 425.56 mV, 1.2932e-10s and 5.5225e-12s, respectively. The parameters of the proposed cell are compared with 6T SRAM [M. Elangovan and K. Gunavathi, Stability analysis of 6T CNTFET SRAM cell for single and multiple CNTs, 2018 4th Int. Conf. Devices, Circuits Syst., Coimbatore, India, 16–17 March 2018, vol. 2, pp. 63–67], 8T SRAM [M. Elangovan, A novel Darlington based 8T CNTFET SRAM cell for low, J. Circuits Syst. Comput.30 (2021) 2150213], 12T SRAM [S. Pal, S. Bose, W. H. Ki and A. Islam, Half-select-free low-power dynamic loop-cutting write assist SRAM cell for space applications, IEEE Trans. Electron Dev. 67 (2020) 80–89, doi:10.1109/TED.2019.2952397], 12T SRAM [N. Yadav, A. P. Shah and S. K. Vishvakarma, Stable, reliable, and bit-interleaving 12T SRAM for space applications: A device circuit co-design, IEEE Trans. Semicond. Manuf. 30 (2017) 276–284, doi:10.1109/TSM.2017.2718029], 12T SRA-M [P. Sharma, S. Gupta, K. Gupta and N. Pandey, A low power subthreshold Schmitt Trigger-based 12T SRAM bit cell with process-variation-tolerant write-ability, Microelectron. J. 97 (2020) 104703, doi:10.1016/j.mejo.2020.104703] and 12T SRAM [P. Sharma, S. Gupta, K. Gupta and N. Pandey, A low power subthreshold Schmitt Trigger based 12T SRAM bit cell with process-variation-tolerant write-ability, Microelectron. J. 97 (2020) 104703, doi:10.1016/j.mejo.2020.104703] cells to understand the performance of the proposed SRAM cell. From the comparative study, it is observed that the proposed cell is more stable than the other cells considered for the comparison and consumes less power in all write, read and hold modes. Also, the read time of the introduced cell is much less than the others. This study also recorded the information on how the performance of an SRAM cell varies as the CNTFET parameters change. The simulation is done with the HSPICE simulation tool using the Stanford University 32nm CNTFET model.

This paper addresses the uncertainty that is present in the design of static random access memory (SRAM) cells using an artificial intelligence (AI) technique. The SRAM has much uncertainty in high-performance portable very large-scale integration (VLSI) chips due to their performance and storage density. This paper presents the way for solving the uncertainty problem by evaluating point-by-point recreation derived for the memory cells inform of the power, speed, and area investment funds acquired in the advanced cell configuration when contrasted with the standard regular architecture for autonomous vehicles using AI algorithm. The adiabatic low power technique is implemented to enrich the configuration of the 6T-SRAM cells. The procedure of the adiabatic process will provide high loss in terms of dissipation of energy which is connected to ground (0V) and transition can be converted from ‘1’ to ‘0’. Moreover, this transition will be decreased to a high amount of degree within corresponding memory cells. Thus uncertainties with the AI model can able to deliver low power reduction using the automatic model of operation as standard adiabatic 6T SRAM cells are implemented. To prove the effectiveness in the reduction of uncertainties a low power margin is obtained with marginal values of 0.25 Volts which is much lesser than the existing models.

This paper investigates the underlying physics of a SRAM device utilizing three-state Quantum Dot Gate (QDG) FETs by building up the physics from the general QDG-FET, its relation to the QDG-Inverter, and ultimately, the QDG-SRAM. The resulting equations from the exploration of the device physics were utilized to create a simulation within SIMULINK. From the simulation, it was found that in addition to being able to store the “1” and “0” states that are customary for an SRAM device, there is also the ability to store an intermediate state and a pseudo-state as a result of the intermediate state, allowing for the possibility of a 2-bit SRAM device in the same spatial constraints of a conventional SRAM unit cell. Additionally, the experimental results of the QDG-SRAM half-cell and the implications of utilizing a 4 state device to create either a 4 state SRAM cell or a 6 state SRAM cell with two pseudo-states are also discussed.

In-Memory Computing (IMC) is an emerging paradigm that aims to shift computational workload away from CPUs. The bit-serial IMC architecture suffers from larger latency when performing logic and arithmetic operations. In this paper, a general-purpose, energy-efficient Bit Parallel IMC Architecture (BP-IMCA) based on Area-Optimized (AO-8T) static random access memory (SRAM) bit-cell is proposed to perform In-Memory Boolean Logic Computation (IMBC) and Near-Memory Arithmetic (NMA) operations with variable bit-width from 1- to 8-bit. The decoupled read/write paths of the employed AO-8T SRAM bit-cell eliminate compute disturbance during IMBC and NMA operations. A self-terminating read word line decoding scheme is proposed to disconnect the RBL discharging path from GND, which decreases the energy consumption of the proposed IMC architecture by 27.71% at 1V for IMBC operations. In addition to this, a $V_{\mathrm{\text{REF}}}$-based Low-offset Symmetric Differential Sense Amplifier (LSDSA) is proposed to achieve fast and reliable sensing for both normal read and IMBC operations in the proposed IMC architecture. Further, a 4Kb SRAM array is implemented in 65-nm technology to analyze the IMC architecture at a supply voltage of 1V. The operating frequency of 1,355MHz and average energy consumption of 7.04fJ/bit is achieved during logic (IMBC) operations. The 8-bit addition and 8-bit multiplication operations achieve an energy efficiency of 11.1 TOPS/W and 2.28 TOPS/W, respectively, at 1V and 970MHz. Cumulatively, the proposed architecture achieves the lowest figure of merit compared to the state-of-the-art IMC architectures.

Carbon nanotubes (CNTs) provide a better alternative of silicon when it comes to nanoscales. Thanks to the high stability and high performance of carbon nanotube, CNT-based FET (CNTFET) devices are gaining popularity of late. They provide high packaging densities. Not much study had been done to analyze the characteristics of CNTFET SRAM in the presence of single event upset (SEU). The aim of this paper is to analyze the way in which SEU brings alterations in CNTFET SRAM characteristics and perform its comparison with respect to CMOS 6T SRAM. This paper analyzes the impact of SEU on voltage and current characteristics in CNTFET-based SRAM cell during three different conditions —read, write and hold modes. In the analysis, CNTFET SRAM showed that even small amount of radiations can cause flipping in cells and special circuitries are required to detect and correct the errors for SEU affected SRAM.

This paper describes the fabrication of quantum dot gate (QDG) $n$-FETs using GeOx-cladded Ge quantum dot self-assembled on tunnel gate oxide. Experimental I–V characteristics exhibiting 4-states are presented. Simulations are presented for the operation of a viable 8-state SRAM using QDG-FETs.

With technology scaling, stability, power dissipation, and device variability, the impact of process, voltage and temperature (PVT) variations has become dominant for static random access memory (SRAM) analysis for productivity and failure. In this paper, ten-transistors (10T) and low power eight-transistors SRAM cells are redesigned using floating-gate MOS transistors (FGMOS). Power centric parameters viz. read power, write power, hold power and delay are the performance analysis metrics. Further, the stochastic parameter variation to study the variability tolerance of the redesigned cell, PVT variations and Monte Carlo simulations have been carried out for 10T FGMOS SRAM cell. Stability has been illustrated with the conventional butterfly method giving read static noise margin (RSNM) and write static noise margin (WSNM) metrics for read stability and write ability, respectively. A comparative analysis with standard six-transistor SRAM cell is carried out. HSPICE simulative analysis has been carried out for 32nm technology node. The redesigned FGMOS SRAM cells provide improved performance. Also, these are robust and reliability efficient with comparable stability.

This paper describes fabrication of Quantum Dot Gate n-FETs using SiOx-cladded Si quantum dot self-assembled on the tunnel gate oxide. Experimental I-V characteristics exhibiting 4-states are presented. Simulation is presented for the operation of viable 4-state SRAMs using QDG-FETs.

This paper describes the fabrication of quantum dot gate (QDG) n-FETs using GeOx-cladded Ge quantum dot self-assembled on tunnel gate oxide. Experimental I–V characteristics exhibiting 4-states are presented. Simulations are presented for the operation of a viable 8-state SRAM using QDG-FETs.

Designing of Complementary Metal Oxide Semiconductor (CMOS) technology based VLSI circuits in deep submicron range includes many challenges like tremendous increase of leakage power. Design is also easily affected by process variation. The Carbon NanoTube Field Effect Transistor (CNTFET) is an alternative for Metal Oxide Semiconductor Field Effect Transistor (MOSFET) for nanoscale range VLSI circuits design. CNTFET offers best performance than MOSFET. It has high stability and consumes least power. Static Random Access Memory (SRAM) cells play a vital role in cache memory in most of the electronic circuits. In this paper, we have proposed a high stable and low power CNTFET based 8Transistor (8T) SRAM cell. The performance of proposed 8T SRAM cells for nominal chiral value (all CNTFET with $m=19$, $n=0$) and Dual chiral value (NCNTFET with $m=19$, $n=0$ and PCNTFET $m=16$, $n=0$) is compared with that of conventional 6T and 8T cells. From the simulation results, it is noted that the proposed structure consumes less power than conventional 6T and 8T cells during read/write operations and gives higher stability during write and hold modes. It consumes higher power than conventional 6T and 8T cells during hold mode and provides lower stability in read mode due to direct contact of bit lines with storage nodes. A comparative analysis of proposed and conventional 8T MOSFET SRAM has been done and the SRAM parameters are tabulated. The simulation is carried out using Stanford University 32nm CNTFET model in HSPICE simulation tool.

The ultimate aim of a memory designer is to design a memory cell which could consume low power with high data stability in the deep nanoscale range. The implementation of Very Large-Scale Integration (VLSI) circuits using MOSFETs in nanoscale range faces many issues such as increasing of leakage power and second-order effects that are easily affected by the PVT variation. Hence, it is essential to find the best alternative of MOSFET for deep submicron design. The Carbon Nanotube Field Effect Transistor (CNTFET) can eradicate all the demerits of MOSFET and be the best replacement of MOSFET for nanoscale range design. In this paper, a 10T CNTFET Static Random Access Memory (SRAM) cell is proposed. The power consumption and Static Noise Margin (SNM) are analyzed. The power consumption and stable performance of the proposed 10T CNTFET SRAM cell are compared with that of conventional 10T CNTFET SRAM cell. The power and stability analyses of the proposed 10T and conventional 10T CNTFET SRAM cells are carried out for the CNTFET parameters such as pitch and chiral vector ($m,n$). The power and SNM analyses are carried out for $\pm$20% variation of oxide thickness (Hox), different dielectric constant (Kox). The supply voltage varies from 0.9V to 0.6V and temperature varies from 27^∘C to 125^∘C. The simulation results show that the proposed 10T CNTFET SRAM cell consumes lesser power than conventional 10T CNTFET SRAM cell during the write, hold and read modes. The write, hold and read stability of the proposed 10T CNTFET SRAM cell are higher as compared with that of conventional 10T CNTFET SRAM. The conventional and proposed 10T SRAM cells are also implemented using MOSFET. The stability and power performance of proposed 10T SRAM cell is also as good as conventional 10T SRAM for MOSFET implementation. The proposed 10T SRAM cell consumes lesser power and gives higher stability than conventional 10T SRAM cell in both CNTFET and MOSFET implementation. The simulation is carried out using Stanford University 32nm CNTFET model in HSPICE simulation tool.

In the current nanoscale regime, fin field effect transistor (FinFET) technology overcomes the limitations of metal oxide semiconductor field effect transistor (MOSFET) technology. Robust and low-power static random access memory (SRAM) design is a demanding task for memory designers, especially in the nanoscale regime. Therefore, this paper proposes a 10 transistor (10T)-based SRAM cell design using low-power FinFET technology. The proposed approach not only reduces the leakage current, but also improves cell stability in different states. The proposed SRAM cell is simulated and analyzed at a 10nm technology node using a multi-gate predictive technology model (PTM) for the transistors with a power supply of 0.7V. The comparison analysis is also presented with the existing designs. The read and write static noise margins, and SRAM electrical quantity matrix (SEQM) of the proposed SRAM cell are improved by 3.54×, 1.71× and 26.41×, respectively, compared with the conventional 6T (C6T) design. The reliability investigations and comparison of the proposed SRAM cell have been carried out using Monte Carlo simulations with $\pm 10$% deviations in the process parameters. The reliability analysis shows that the proposed SRAM cell is less sensitive to process variations.

A single-ended six-transistor (6T) SRAM cell composed of a five-transistor (5T) cell and a read-assist low-$V_{\mathrm{\text{th}}}$ PMOS as foot switch to prevent leakage damaging the data state is proposed in this work. Besides, a power–delay product (PDP) reduction circuitry design for nanoscale SRAMs is also proposed. The proposed PDP reduction circuitry design is composed of an adaptive voltage detection (AVD) circuit generating a boost-enable signal if the process variation is over a predefined range and a half-period word-line boosting (HWB) circuit responding to the enable signal. The proposed SRAM is implemented using TSMC 28-nm CMOS logic technology. PDP reduction is verified to be 41.73% according to the measurement results. The energy per access is 0.0206 pJ given the 800-mV power supply and 40-MHz system clock rate.

This paper presents the design and simulation of static random access memory (SRAM) using two channel spatial wavefunction switched field-effect transistor (SWS-FET), also known as a twin-drain metal oxide semiconductor field effect transistor (MOS-FET). In the SWS-FET, the channel between source and drain has two quantum well layers separated by a high band gap material between them. The gate voltage controls the charge carrier concentration in the quantum well layers and it causes the switching of charge carriers from one channel to other channel of the device. The standard SRAM circuit has six transistors (6T), two p-type MOS-FET and four n-type MOS-FET. By using the SWSFET, the size and the number of transistors are reduced and all of transistors are n-channel SWS-FET. This paper proposes two different models of the SWS-FET SRAM circuits with three transistors (3T) and four transistors (4T) also addresses the stability of the proposed SWS-FET SRAM circuits by using the N-curve analysis. The proposed models are based on integration between Berkeley Shortchannel IGFET Model (BSIM) and Analog Behavioral Model (ABM), the model is suitable to investigate the gates configuration and transient analysis at circuit level.

Leakage power reduction of an SRAM-based look-up table (LUT) in field-programmable gate array (FPGA) has been achieved in this work by implementing an efficient and dynamic power gating technique. The logic of gating is based on the theory of automatically shutting down the power supply to the inactive blocks of LUT during runtime, contrary to all previous works which involved manual intervention for the implementation of power gating. Two techniques of power gating are introduced in this work, PG1 and PG2. PG1 results in more power savings than PG2, however, PG2 has an advantage of low area overhead. Simulation has been carried out for all possible input combinations of LUT, designed in Cadence Virtuoso tool at 45nm technology. The results indicate a leakage power reduction of up to 50% in PG1 technique, with an average area overhead of 14.15%. The power savings in PG2 is up to 38%, with a minimal increase in area of 1.76%. The power bounce noise is also analyzed for the proposed techniques and reported.