This paper investigates the leakage current, static noise margin (SNM), delay and energy consumption of a 6 transistor FinFET based static random-access memory (SRAM) cell due to the variation in design and operating parameters of the SRAM cell. The SRAM design and operating parameters considered in this investigation are transistor sizing, supply voltage, word-line voltage, temperature and PFET and NFET back gate biasing. This investigation is performed using a 11nm FinFET shorted gate and low power technology models. Based on the investigation results, we propose a robust 6 transistor SRAM cells with optimized performance using shorted gate and independent gate low power FinFET models. By optimizing the design parameters of the cell, the shorted-gate design shows an improvement of read SNM of 261.56mV and an improvement of hold SNM of 87.68mV when compared to a shorted-gate cell with standard design parameters. The low-power design shows an improvement of read SNM of 146.18mV and a marginal decrease in hold SNM of 22.84mV when compared to a low-power cell with standard design parameters. Both the cells with the new optimized design parameters are shown to improve the overall SNM of the cells with minimal impact on the subthreshold leakage currents, performance and energy consumption.
This paper investigates the leakage current, static noise margin (SNM), delay and energy consumption of a 6 transistor FinFET based static random-access memory (SRAM) cell due to the variation in design and operating parameters of the SRAM cell. The SRAM design and operating parameters considered in this
investigation are transistor sizing, supply voltage, word-line voltage, temperature and PFET and NFET back gate biasing. This investigation is performed using a 11nm FinFET shorted gate and low power technology models. Based on the investigation results, we propose a robust 6 transistor SRAM cells with optimized performance using shorted gate and independent gate low power FinFET models. By optimizing
the design parameters of the cell, the shorted-gate design shows an improvement of read SNM of 261.56mV
and an improvement of hold SNM of 87.68mV when compared to a shorted-gate cell with standard design parameters. The low-power design shows an improvement of read SNM of 146.18mV and a marginal decrease in hold SNM of 22.84mV when compared to a low-power cell with standard design parameters. Both the cells with the new optimized design parameters are shown to improve the overall SNM of the cells
with minimal impact on the subthreshold leakage currents, performance and energy consumption.
250nm Technology Based Low Power SRAM Memoryiosrjce
High integration density, low power and fastperformance are all critical parameters in designing of
memory blocks. Static Random Access Memories (SRAMs)’s focusing on optimizing dynamic power concept of
virtual source transistors is used for removing direct connection between VDD and GND.
Also stacking effect can be reduced by switching off the stacktransistors when the memory is ideal and the
leakage current using SVL techniques This paper discusses the evolution of 9t SRAM circuits in terms of low
power consumption, The whole circuit verification is done on the Tanner tool, Schematic of the
SRAM cell is designed on the S-Edit and net list simulation done by using T-spice and waveforms are analyzed
through the W-edit
Static-Noise-Margin Analysis of Modified 6T SRAM Cell during Read Operationidescitation
As modern technology is spreading fast, it is very
important to design low power, high performance, fast
responding SRAM(Static Random Access Memory) since they
are critical component in high performance processors. In
this paper we discuss about the noise effect of different SRAM
circuits during read operation which hinders the stability of
the SRAM cell. This paper also represents a modified 6T
SRAM cell which increases the cell stability without
increasing transistor count.
Design and Simulation Low power SRAM Circuitsijsrd.com
SRAMs), focusing on optimizing delay and power. As the scaling trends in the speed and power of SRAMs with size and technology and find that the SRAM delay scales as the logarithm of its size as long as the interconnect delay is negligible. Non-scaling of threshold mismatches with process scaling, causes the signal swings in the bitlines and data lines also not to scale, leading to an increase in the relative delay of an SRAM, across technology generations. Appropriate methods for reduction of power consumption were studied such as capacitance reduction, very low operating voltages, DC and AC current reduction and suppression of leakage currents to name a few.. Many of reviewed techniques are applicable to other applications such as ASICs, DSPs, etc. Battery and solar-cell operation requires an operating voltage environment in low voltage area. These conditions demand new design approaches and more sophisticated concepts to retain high device reliability. The proposed techniques (USRS and LPRS) are topology based and hence easier to implement.
Designed a fully customized 128x10b SRAM by constructing schematic & virtuoso layout of memory cell array (6T cell), row & column decoder, pre-charge circuit, write circuit and sense amplifier using Cadence. Manually placed and routed all components, performed DRC & LVS debugging of constructed schematic and layout and ran PEX to generate the final Netlist, Hspice Spectre simulation of final design for verification of the correct functionality and analysis of best read, best write cycles & the worst case timing for read and write. Timing and power consumed is analyzed through STA-Primetime (Static timing Analysis)
Implementation of High Reliable 6T SRAM Cell Designiosrjce
Memory can be formed with the integration of large number of basic storing element called cells.
SRAM cell is one of the basic storing unit of volatile semiconductor memory that stores binary logic '1' or '0' bit.
Modified read and write circuits were proposed in this paper to address incorrect read and write operations in
conventional 6T SRAM cell design available in open literature. Design of a new highly reliable 6T SRAM cell
design is proposed with reliable read, write operations and negative bit line voltage (NBLV). Simulations are
carried out using MENTOR GRAPHICS
This paper investigates the leakage current, static noise margin (SNM), delay and energy consumption of a 6 transistor FinFET based static random-access memory (SRAM) cell due to the variation in design and operating parameters of the SRAM cell. The SRAM design and operating parameters considered in this
investigation are transistor sizing, supply voltage, word-line voltage, temperature and PFET and NFET back gate biasing. This investigation is performed using a 11nm FinFET shorted gate and low power technology models. Based on the investigation results, we propose a robust 6 transistor SRAM cells with optimized performance using shorted gate and independent gate low power FinFET models. By optimizing
the design parameters of the cell, the shorted-gate design shows an improvement of read SNM of 261.56mV
and an improvement of hold SNM of 87.68mV when compared to a shorted-gate cell with standard design parameters. The low-power design shows an improvement of read SNM of 146.18mV and a marginal decrease in hold SNM of 22.84mV when compared to a low-power cell with standard design parameters. Both the cells with the new optimized design parameters are shown to improve the overall SNM of the cells
with minimal impact on the subthreshold leakage currents, performance and energy consumption.
250nm Technology Based Low Power SRAM Memoryiosrjce
High integration density, low power and fastperformance are all critical parameters in designing of
memory blocks. Static Random Access Memories (SRAMs)’s focusing on optimizing dynamic power concept of
virtual source transistors is used for removing direct connection between VDD and GND.
Also stacking effect can be reduced by switching off the stacktransistors when the memory is ideal and the
leakage current using SVL techniques This paper discusses the evolution of 9t SRAM circuits in terms of low
power consumption, The whole circuit verification is done on the Tanner tool, Schematic of the
SRAM cell is designed on the S-Edit and net list simulation done by using T-spice and waveforms are analyzed
through the W-edit
Static-Noise-Margin Analysis of Modified 6T SRAM Cell during Read Operationidescitation
As modern technology is spreading fast, it is very
important to design low power, high performance, fast
responding SRAM(Static Random Access Memory) since they
are critical component in high performance processors. In
this paper we discuss about the noise effect of different SRAM
circuits during read operation which hinders the stability of
the SRAM cell. This paper also represents a modified 6T
SRAM cell which increases the cell stability without
increasing transistor count.
Design and Simulation Low power SRAM Circuitsijsrd.com
SRAMs), focusing on optimizing delay and power. As the scaling trends in the speed and power of SRAMs with size and technology and find that the SRAM delay scales as the logarithm of its size as long as the interconnect delay is negligible. Non-scaling of threshold mismatches with process scaling, causes the signal swings in the bitlines and data lines also not to scale, leading to an increase in the relative delay of an SRAM, across technology generations. Appropriate methods for reduction of power consumption were studied such as capacitance reduction, very low operating voltages, DC and AC current reduction and suppression of leakage currents to name a few.. Many of reviewed techniques are applicable to other applications such as ASICs, DSPs, etc. Battery and solar-cell operation requires an operating voltage environment in low voltage area. These conditions demand new design approaches and more sophisticated concepts to retain high device reliability. The proposed techniques (USRS and LPRS) are topology based and hence easier to implement.
Designed a fully customized 128x10b SRAM by constructing schematic & virtuoso layout of memory cell array (6T cell), row & column decoder, pre-charge circuit, write circuit and sense amplifier using Cadence. Manually placed and routed all components, performed DRC & LVS debugging of constructed schematic and layout and ran PEX to generate the final Netlist, Hspice Spectre simulation of final design for verification of the correct functionality and analysis of best read, best write cycles & the worst case timing for read and write. Timing and power consumed is analyzed through STA-Primetime (Static timing Analysis)
Implementation of High Reliable 6T SRAM Cell Designiosrjce
Memory can be formed with the integration of large number of basic storing element called cells.
SRAM cell is one of the basic storing unit of volatile semiconductor memory that stores binary logic '1' or '0' bit.
Modified read and write circuits were proposed in this paper to address incorrect read and write operations in
conventional 6T SRAM cell design available in open literature. Design of a new highly reliable 6T SRAM cell
design is proposed with reliable read, write operations and negative bit line voltage (NBLV). Simulations are
carried out using MENTOR GRAPHICS
In the project#1, IBM 130nm process is used to design and manual layout a 128 word SRAM, with word size 10bits. Cadence's Virtuoso is applied for layout editing, DRC and LVS running and circuit simulation.
This is a project implemented in VHDL. It is design of a multi-level cache memory for a uni-processor system. The document also includes some of the simulation and synthesis results.
Analysis and Simulation of Sub-threshold Leakage Current in P3 SRAM Cell at D...IDES Editor
In this work, the analysis and simulation work is
proposed for the low-power (reduced subthreshold leakage)
and high performance SRAM bit-cells for mobile multimedia
applications in deep-sub-micron (DSM) CMOS technology.
The sub-threshold leakage analysis of the P3 SRAM cell has
been carried out. It has been observed that due to pMOS
stacking and full supply body-biasing, there is a reduction of
70% and 86% in sub-threshold leakage current at VDD=0.8V
and VDD=0.7V respectively as compared to conventional 6T
SRAM cell. Due to this a reduction in the standby power has
been achieved w.r.t the 6T and PP SRAM design at a bearable
expense of the SVNM and the WTV.
Design of a 64-bit ultra low latency memory using 6T SRAM cells and PDK 45nm technology on CADENCE to simulate the results of our chosen implementation.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Energy optimization of 6T SRAM cell using low-voltage and high-performance in...IJECEIAES
The performance of the cell deteriorates, when static random access memory (SRAM) cell is operated below 1V supply voltage with continuous scale down of the complementary metal oxide semiconductor (CMOS) technology. The conventional 6T, 8T-SRAM cells suffer writeability and read static noise margins (SNM) at low-voltages leads to degradation of cell stability. To improve the cell stability and reduce the dynamic power dissipation at low- voltages of the SRAM cell, we proposed four SRAM cells based on inverter structures with less energy consumption using voltage divider bias current sink/source inverter and NOR/NAND gate using a pseudo-nMOS inverter. The design and implementation of SRAM cell using proposed inverter structures are compared with standard 6T, 8T and ST-11T SRAM cells for different supply voltages at 22-nm CMOS technology exhibit better performance of the cell. The read/write static noise margin of the cell significantly increases due to voltage divider bias network built with larger cell-ratio during read path. The load capacitance of the cell is reduced with minimized switching transitions of the devices during high-to-low and low- to-high of the pull-up and pull-down networks from VDD to ground leads to on an average 54% of dynamic power consumption. When compared with the existing ones, the read/write power of the proposed cells is reduced to 30%. The static power gets reduced by 24% due to stacking of transistors takes place in the proposed SRAM cells as compare to existing ones. The layout of the proposed cells is drawn at a 45-nm technology, and occupies an area of 1.5 times greater and 1.8 times greater as compared with 6T-SRAM cell.
STUDY OF SPIN TRANSFER TORQUE (STT) AND SPIN ORBIT TORQUE (SOT) MAGNETIC TUNN...elelijjournal
Magnetic Random Access Memory (MRAM) is a promising candidate to be the universal non-volatile (NV) storage device. The Magnetic Tunnel Junction (MTJ) is the cornerstone of the NV-MRAM technology. 2- terminal MTJ based on Spin Transfer Torque (STT) switching is considered as a hot topic for academic and industrial researchers. Moreover, the 3-terminal Spin Orbit Torque (SOT) MTJ has recently been considered as a hopeful device which provides an increased reliability thanks to independent write and read paths. Since both MTJ devices (STT and SOT) seem to revolutionize the data storage market, it is necessary to explore their compatibility with very advanced CMOS processes in terms of transistor sizing and performance. Assuming a good maturity of the magnetic processes that would enable to fabricate small junctions, simulation results show that the existing advanced sub-micronic CMOS processes can drive the required writing current with reasonable size of transistors confirming the high density feature of MRAMs. At 28 nm node, the minimum transistor size can be used by the STT device. The SOT device shows remarkable energy efficiency with 6× improvement compared with the STT technology. Results are very encouraging for future complex hybrid magnetic/CMOS integrated circuits (ICs).
This paper investigates the leakage current, static noise margin (SNM), delay and energy consumption of a
6 transistor FinFET based static random-access memory (SRAM) cell due to the variation in design and
operating parameters of the SRAM cell. The SRAM design and operating parameters considered in this
investigation are transistor sizing, supply voltage, word-line voltage, temperature and PFET and NFET
back gate biasing. This investigation is performed using a 11nm FinFET shorted gate and low power
technology models. Based on the investigation results, we propose a robust 6 transistor SRAM cells with
optimized performance using shorted gate and independent gate low power FinFET models. By optimizing
the design parameters of the cell, the shorted-gate design shows an improvement of read SNM of 261.56mV
and an improvement of hold SNM of 87.68mV when compared to a shorted-gate cell with standard design
parameters. The low-power design shows an improvement of read SNM of 146.18mV and a marginal
decrease in hold SNM of 22.84mV when compared to a low-power cell with standard design parameters.
Both the cells with the new optimized design parameters are shown to improve the overall SNM of the cells
with minimal impact on the subthreshold leakage currents, performance and energy consumption.
This paper investigates the leakage current, static noise margin (SNM), delay and energy consumption of a
6 transistor FinFET based static random-access memory (SRAM) cell due to the variation in design and
operating parameters of the SRAM cell. The SRAM design and operating parameters considered in this
investigation are transistor sizing, supply voltage, word-line voltage, temperature and PFET and NFET
back gate biasing. This investigation is performed using a 11nm FinFET shorted gate and low power
technology models. Based on the investigation results, we propose a robust 6 transistor SRAM cells with
optimized performance using shorted gate and independent gate low power FinFET models. By optimizing
the design parameters of the cell, the shorted-gate design shows an improvement of read SNM of 261.56mV
and an improvement of hold SNM of 87.68mV when compared to a shorted-gate cell with standard design
parameters. The low-power design shows an improvement of read SNM of 146.18mV and a marginal
decrease in hold SNM of 22.84mV when compared to a low-power cell with standard design parameters.
Both the cells with the new optimized design parameters are shown to improve the overall SNM of the cells
with minimal impact on the subthreshold leakage currents, performance and energy consumption.
This paper investigates the leakage current, static noise margin (SNM), delay and energy consumption of a 6 transistor FinFET based static random-access memory (SRAM) cell due to the variation in design and operating parameters of the SRAM cell. The SRAM design and operating parameters considered in this
investigation are transistor sizing, supply voltage, word-line voltage, temperature and PFET and NFET back gate biasing. This investigation is performed using a 11nm FinFET shorted gate and low power technology models. Based on the investigation results, we propose a robust 6 transistor SRAM cells with optimized performance using shorted gate and independent gate low power FinFET models. By optimizing
the design parameters of the cell, the shorted-gate design shows an improvement of read SNM of 261.56mV and an improvement of hold SNM of 87.68mV when compared to a shorted-gate cell with standard design parameters. The low-power design shows an improvement of read SNM of 146.18mV and a marginal decrease in hold SNM of 22.84mV when compared to a low-power cell with standard design parameters.
Both the cells with the new optimized design parameters are shown to improve the overall SNM of the cells with minimal impact on the subthreshold leakage currents, performance and energy consumption.
Implementation of an Efficient SRAM for Ultra-Low Voltage Application Based o...IOSR Journals
Abstract: Operation of standard 6T static random access memory (SRAM) cells at sub or near threshold
voltages is unfeasible, predominantly due to degraded static noise margins (SNM) and poor robustness. We
analyze Schmitt-Trigger (ST)-based differential-sensing static random access memory (SRAM) bitcells for
ultralow-voltage operation. The ST-based SRAM bitcells address the fundamental conflicting design
requirement of the read versus write operation of a conventional 6T bitcell. The ST operation gives better readstability
as well as better write-ability compared to the standard 6T bitcell. In this paper we are going to
propose a new SRAM bitcell for the purpose of read stability and write ability by using 90nm technology , and
less power consumption, less area than the existing Schmitt trigger1 based SRAM. Design and simulations were done using DSCH and Microwind.
Index Terms: read stability, write ability, Schmitt trigger.
In the project#1, IBM 130nm process is used to design and manual layout a 128 word SRAM, with word size 10bits. Cadence's Virtuoso is applied for layout editing, DRC and LVS running and circuit simulation.
This is a project implemented in VHDL. It is design of a multi-level cache memory for a uni-processor system. The document also includes some of the simulation and synthesis results.
Analysis and Simulation of Sub-threshold Leakage Current in P3 SRAM Cell at D...IDES Editor
In this work, the analysis and simulation work is
proposed for the low-power (reduced subthreshold leakage)
and high performance SRAM bit-cells for mobile multimedia
applications in deep-sub-micron (DSM) CMOS technology.
The sub-threshold leakage analysis of the P3 SRAM cell has
been carried out. It has been observed that due to pMOS
stacking and full supply body-biasing, there is a reduction of
70% and 86% in sub-threshold leakage current at VDD=0.8V
and VDD=0.7V respectively as compared to conventional 6T
SRAM cell. Due to this a reduction in the standby power has
been achieved w.r.t the 6T and PP SRAM design at a bearable
expense of the SVNM and the WTV.
Design of a 64-bit ultra low latency memory using 6T SRAM cells and PDK 45nm technology on CADENCE to simulate the results of our chosen implementation.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Energy optimization of 6T SRAM cell using low-voltage and high-performance in...IJECEIAES
The performance of the cell deteriorates, when static random access memory (SRAM) cell is operated below 1V supply voltage with continuous scale down of the complementary metal oxide semiconductor (CMOS) technology. The conventional 6T, 8T-SRAM cells suffer writeability and read static noise margins (SNM) at low-voltages leads to degradation of cell stability. To improve the cell stability and reduce the dynamic power dissipation at low- voltages of the SRAM cell, we proposed four SRAM cells based on inverter structures with less energy consumption using voltage divider bias current sink/source inverter and NOR/NAND gate using a pseudo-nMOS inverter. The design and implementation of SRAM cell using proposed inverter structures are compared with standard 6T, 8T and ST-11T SRAM cells for different supply voltages at 22-nm CMOS technology exhibit better performance of the cell. The read/write static noise margin of the cell significantly increases due to voltage divider bias network built with larger cell-ratio during read path. The load capacitance of the cell is reduced with minimized switching transitions of the devices during high-to-low and low- to-high of the pull-up and pull-down networks from VDD to ground leads to on an average 54% of dynamic power consumption. When compared with the existing ones, the read/write power of the proposed cells is reduced to 30%. The static power gets reduced by 24% due to stacking of transistors takes place in the proposed SRAM cells as compare to existing ones. The layout of the proposed cells is drawn at a 45-nm technology, and occupies an area of 1.5 times greater and 1.8 times greater as compared with 6T-SRAM cell.
STUDY OF SPIN TRANSFER TORQUE (STT) AND SPIN ORBIT TORQUE (SOT) MAGNETIC TUNN...elelijjournal
Magnetic Random Access Memory (MRAM) is a promising candidate to be the universal non-volatile (NV) storage device. The Magnetic Tunnel Junction (MTJ) is the cornerstone of the NV-MRAM technology. 2- terminal MTJ based on Spin Transfer Torque (STT) switching is considered as a hot topic for academic and industrial researchers. Moreover, the 3-terminal Spin Orbit Torque (SOT) MTJ has recently been considered as a hopeful device which provides an increased reliability thanks to independent write and read paths. Since both MTJ devices (STT and SOT) seem to revolutionize the data storage market, it is necessary to explore their compatibility with very advanced CMOS processes in terms of transistor sizing and performance. Assuming a good maturity of the magnetic processes that would enable to fabricate small junctions, simulation results show that the existing advanced sub-micronic CMOS processes can drive the required writing current with reasonable size of transistors confirming the high density feature of MRAMs. At 28 nm node, the minimum transistor size can be used by the STT device. The SOT device shows remarkable energy efficiency with 6× improvement compared with the STT technology. Results are very encouraging for future complex hybrid magnetic/CMOS integrated circuits (ICs).
This paper investigates the leakage current, static noise margin (SNM), delay and energy consumption of a
6 transistor FinFET based static random-access memory (SRAM) cell due to the variation in design and
operating parameters of the SRAM cell. The SRAM design and operating parameters considered in this
investigation are transistor sizing, supply voltage, word-line voltage, temperature and PFET and NFET
back gate biasing. This investigation is performed using a 11nm FinFET shorted gate and low power
technology models. Based on the investigation results, we propose a robust 6 transistor SRAM cells with
optimized performance using shorted gate and independent gate low power FinFET models. By optimizing
the design parameters of the cell, the shorted-gate design shows an improvement of read SNM of 261.56mV
and an improvement of hold SNM of 87.68mV when compared to a shorted-gate cell with standard design
parameters. The low-power design shows an improvement of read SNM of 146.18mV and a marginal
decrease in hold SNM of 22.84mV when compared to a low-power cell with standard design parameters.
Both the cells with the new optimized design parameters are shown to improve the overall SNM of the cells
with minimal impact on the subthreshold leakage currents, performance and energy consumption.
This paper investigates the leakage current, static noise margin (SNM), delay and energy consumption of a
6 transistor FinFET based static random-access memory (SRAM) cell due to the variation in design and
operating parameters of the SRAM cell. The SRAM design and operating parameters considered in this
investigation are transistor sizing, supply voltage, word-line voltage, temperature and PFET and NFET
back gate biasing. This investigation is performed using a 11nm FinFET shorted gate and low power
technology models. Based on the investigation results, we propose a robust 6 transistor SRAM cells with
optimized performance using shorted gate and independent gate low power FinFET models. By optimizing
the design parameters of the cell, the shorted-gate design shows an improvement of read SNM of 261.56mV
and an improvement of hold SNM of 87.68mV when compared to a shorted-gate cell with standard design
parameters. The low-power design shows an improvement of read SNM of 146.18mV and a marginal
decrease in hold SNM of 22.84mV when compared to a low-power cell with standard design parameters.
Both the cells with the new optimized design parameters are shown to improve the overall SNM of the cells
with minimal impact on the subthreshold leakage currents, performance and energy consumption.
This paper investigates the leakage current, static noise margin (SNM), delay and energy consumption of a 6 transistor FinFET based static random-access memory (SRAM) cell due to the variation in design and operating parameters of the SRAM cell. The SRAM design and operating parameters considered in this
investigation are transistor sizing, supply voltage, word-line voltage, temperature and PFET and NFET back gate biasing. This investigation is performed using a 11nm FinFET shorted gate and low power technology models. Based on the investigation results, we propose a robust 6 transistor SRAM cells with optimized performance using shorted gate and independent gate low power FinFET models. By optimizing
the design parameters of the cell, the shorted-gate design shows an improvement of read SNM of 261.56mV and an improvement of hold SNM of 87.68mV when compared to a shorted-gate cell with standard design parameters. The low-power design shows an improvement of read SNM of 146.18mV and a marginal decrease in hold SNM of 22.84mV when compared to a low-power cell with standard design parameters.
Both the cells with the new optimized design parameters are shown to improve the overall SNM of the cells with minimal impact on the subthreshold leakage currents, performance and energy consumption.
Implementation of an Efficient SRAM for Ultra-Low Voltage Application Based o...IOSR Journals
Abstract: Operation of standard 6T static random access memory (SRAM) cells at sub or near threshold
voltages is unfeasible, predominantly due to degraded static noise margins (SNM) and poor robustness. We
analyze Schmitt-Trigger (ST)-based differential-sensing static random access memory (SRAM) bitcells for
ultralow-voltage operation. The ST-based SRAM bitcells address the fundamental conflicting design
requirement of the read versus write operation of a conventional 6T bitcell. The ST operation gives better readstability
as well as better write-ability compared to the standard 6T bitcell. In this paper we are going to
propose a new SRAM bitcell for the purpose of read stability and write ability by using 90nm technology , and
less power consumption, less area than the existing Schmitt trigger1 based SRAM. Design and simulations were done using DSCH and Microwind.
Index Terms: read stability, write ability, Schmitt trigger.
This paper presents a spin-transfer torque- magnetic
tunnel junction (STT-MTJ) based non-volatile 9-transistor
(9T) SRAM cell. The cell achieves low power dissipation due
to its series connected MTJ elements and read buffer which
offer stacking effect. The paper studies the impact of PVT
(process, voltage, and temperature) variations on the design
metric of the SRAM cell such as write delay and compares the
results with non-volatile 8T SRAM cell (NV8T). The proposed
design consumes lower leakage power and exhibits narrower
spread in write delay compared with NV8T.
A Single-Ended With Dynamic Feedback Control 8T Subthreshold SRAM Cell Ieee Xpert
A Single-Ended With Dynamic Feedback Control
8T Subthreshold SRAM Cell
A Single-Ended With Dynamic Feedback Control
8T Subthreshold SRAM Cell
A Single-Ended With Dynamic Feedback Control
8T Subthreshold SRAM Cell
A Single-Ended With Dynamic Feedback Control
8T Subthreshold SRAM Cell
A Single-Ended With Dynamic Feedback Control
8T Subthreshold SRAM Cell
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Comparitive Analysis of Improved 6t Sram Cell With Different Sram CellIJERA Editor
High speed and low power consumption have been the primary issue to design Static Random Access Memory (SRAM), but we are facing new challenges with the scaling of technology. The stability and speed of SRAM are important issues to improve efficiency and performance of the system. Stability of the SRAM depends on the static noise margin (SNM) so the noise margin is also important parameter for the design of memory because the higher noise margin confirms the high speed of the SRAM cell. In this paper, the improved 6T SRAM cell shows maximum reduction in power consumption of 88%, maximum reduction in delay of 64% and maximum SNM of 17% increases compared with 7T SRAM cell.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Design of STT-RAM cell in 45nm hybrid CMOS/MTJ processEditor IJCATR
This paper evaluates the performance of Spin-Torque Transfer Random Access Memory (STT-RAM) basic memory cell
configurations in 45nm hybrid CMOS/MTJ process. Switching speed and current drawn by the cells have been calculated and
compared. Cell design has been done using cadence tools. The results obtained show good agreement with theoretical results.
Similar to STATIC NOISE MARGIN OPTIMIZED 11NM SHORTED-GATE AND INDEPENDENT-GATE LOW POWER 6T FINFET SRAM TOPOLOGIES (20)
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STATIC NOISE MARGIN OPTIMIZED 11NM SHORTED-GATE AND INDEPENDENT-GATE LOW POWER 6T FINFET SRAM TOPOLOGIES
1. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
DOI: 10.5121/vlsic.2018.9501 1
STATIC NOISE MARGIN OPTIMIZED 11NM
SHORTED-GATE AND INDEPENDENT-GATE LOW
POWER 6T FINFET SRAM TOPOLOGIES
DustenVernor, Santosh Koppa and Eugene John
Department of Electrical andComputer Engineering
University of Texas at San Antonio, Texas, USA
ABSTRACT
This paper investigates the leakage current, static noise margin (SNM), delay and energy consumption of a
6 transistor FinFET based static random-access memory (SRAM) cell due to the variation in design and
operating parameters of the SRAM cell. The SRAM design and operating parameters considered in this
investigation are transistor sizing, supply voltage, word-line voltage, temperature and PFET and NFET
back gate biasing. This investigation is performed using a 11nm FinFET shorted gate and low power
technology models. Based on the investigation results, we propose a robust 6 transistor SRAM cells with
optimized performance using shorted gate and independent gate low power FinFET models. By optimizing
the design parameters of the cell, the shorted-gate design shows an improvement of read SNM of 261.56mV
and an improvement of hold SNM of 87.68mV when compared to a shorted-gate cell with standard design
parameters. The low-power design shows an improvement of read SNM of 146.18mV and a marginal
decrease in hold SNM of 22.84mV when compared to a low-power cell with standard design parameters.
Both the cells with the new optimized design parameters are shown to improve the overall SNM of the cells
with minimal impact on the subthreshold leakage currents, performance and energy consumption.
KEYWORDS
SRAM, Leakage Power, Write Delay, Read Delay, FinFET, Static Noise Margin, SNM, Back GateBiasing.
1. INTRODUCTION
Technology scaling in bulk-Si MOSFETs presents a growing concern toward the stability of
Static Random Access Memories (SRAM) [1]. As the feature size of the technology decreases,
the process and operating parameter variations can negatively impact the reliability of the
memory to retain data [2]. Higher static noise margin (SNM) during read, write, and hold
operations are required for low-voltage low power SRAM designs to mitigate the effects of
process and operating parameter variations on data stored in SRAM cells. In single supply voltage
integrated circuits (ICs), the supply voltage (Vdd) is typically defined by the SRAM’s static noise
margin [3]. To improve the electrostatic characteristics of the gate, a multi-gate device structure
can be utilized, as they provide improved electrostatic control of the gate [3].
Unlike conventional MOSFETs, FinFETs employ a three-dimensional gate structure, which
allows for better electrostatic control of the gate. By raising the channel above the surface of the
wafer, the gate wraps around the channel to provide greater control over the channel as shown in
Figure 1. The self-aligned gate straddles a narrow silicon fin (channel), therefore current flows
parallel to the wafer surface. The major obstacle when moving from bulk CMOS to multi-gate
devices lies within the manufacturing. However, FinFETs offer a geometry that is compatible
with current manufacturing techniques [4]. This presents a distinct advantage in the cost of
production and has helped to facilitate the use of FinFET technology in the sub-20nm regime.
2. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
2
This paper focuses on bridging the gap between the research performed to transfer from 6T
CMOS SRAM memory cells to 6T FinFET SRAM memory cells, and analyze the tradeoffs of
varying cell ratio, pull-up ratio and word line voltage effect on the stability of a shorted-gate (SG)
and independent-gate low-power (LP) 6T FinFET SRAM memory cells.
Figure 1: 2-d Planar MOSFET (left), 3-d FinFET (right) [5].
The rest of this paper is organized as follows. Section 2 describes the tools and methodologies
used for the simulations of the SRAMs. Section 3 presents the results and discussions of the
variations in the design and operating parameters such as cell ratio, pull-up ratio, supply voltage,
word-line voltage, temperature and back gate biasing on the SRAM subthreshold leakage
currents, SNM and delays for SG and LP schemes. Section 4 describes the optimization of the
design parameters for the SRAM cell using SG and LP models. Finally, conclusions are presented
in section 5.
2. TOOLS AND METHODOLOGIES
In this research, simulation of the SRAM cells utilizes the University of Florida’s Spice-3-UFDG
fully-depleted(FD) silicon-on-insulator(SOI) FinFET model [6]. This FinFET model accounts for
the short channel effects based on the process and physics of the compact model. Simulations are
performed using Ngspice simulator which is a mixed mode-mixed level circuit simulator. The
SRAM cells are simulated using shorted-gate and independent-gate mode FinFET models. The
parameters of the FinFETs used in this research are summarized in Table 1.
TABLE 1: 6T FinFET design parameters
Parameter Value
Orientation <110>
Gate Length 1.07 nm
Fin Height 1.8 nm
Oxide Thickness 0.59 nm/cm3
Source & Drain Doping 1E15/cm3
Vdd,Word Line 0.68 V
Cell Ratio, Pull-Up Ratio 1:1, 1:1
PFET Back-Gate Biasing 0.88 V
NFET Back-Gate Biasing -0.2 V
The design parameters considered in the simulations of the SRAM are transistor sizing (cell ratio
and pull-up ratio), supply voltage, word-line voltage, temperature, PFET and NFET back-gate
biasing.
3. International Journal of VLSI design & Communication Systems
The cell ratio (βC) and load ratio (
𝛽 =
𝑊
𝐿
𝑊
𝐿
=
𝑊
𝑊
𝛽 =
𝑊
𝐿
𝑊
𝐿
=
𝑊
𝑊
where WMiand LMi are the width and length of the
the transistors in the cell are the same, only the width is considered in the ratio. For
destructive read and write operations, appropriate βC and βL must be selected.
Subthreshold leakage, hold SNM, read SNM, read delay and write delay are measured by varying
one of the above-mentioned design parameters while keeping the other design parameters
constant. After analysing the results, an optimized SRAM cell with maximum S
subthreshold leakage and delay is proposed for both SG and LP 6T FinFET SRAM cells.
SNM calculations were performed by
with noise sources inserted between them [
equal and opposite in direction, which represents the worst
To measure the SNM, the “butterfly curve” method was employed [
transfer curve (VTC) of one inverter is
transposed onto it. This creates a “butterfly curve”, as seen in Figure 2 (b). The SNM of the
SRAM cell is the length of the diagonal of the maximum square that can fit in the butterfly curve
[8]- [12]. The back-gate biasing simulations are performed only for LP models which has two
independent gates (front and back gates) while the gates are shorted for SG models.
Figure 2: (a) Schematic diagram for finding SNM [1], (b) Butterfly diagram for fi
3. SIMULATION RESULTS AND
The SRAM cell using SG and LP FinFET models are simulated with one of the design parameters
mentioned in the previous section varies while the other parameters are static. For each simulation
run, the subthreshold leakage current, hold SNM, read SNM, read
International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, Oc
ratio (βL) are given by equations (1) and (2) respectively.
𝐿
𝐿
=
𝑊
𝑊
=
𝑊
𝑊
(
𝐿
𝐿
=
𝑊
𝑊
=
𝑊
𝑊
(2
are the width and length of the ith
transistor. Because the channel length of all
the transistors in the cell are the same, only the width is considered in the ratio. For
ead and write operations, appropriate βC and βL must be selected.
Subthreshold leakage, hold SNM, read SNM, read delay and write delay are measured by varying
mentioned design parameters while keeping the other design parameters
the results, an optimized SRAM cell with maximum S
subthreshold leakage and delay is proposed for both SG and LP 6T FinFET SRAM cells.
SNM calculations were performed by modelling the SRAM cell as two cross-coupled inverters
with noise sources inserted between them [7], shown in Figure 2(a). Both the noise voltages are
equal and opposite in direction, which represents the worst-case scenario for noise margins [
To measure the SNM, the “butterfly curve” method was employed [8]. In this method, the voltage
transfer curve (VTC) of one inverter is plotted with the inverse of the VTC of the other inverter
transposed onto it. This creates a “butterfly curve”, as seen in Figure 2 (b). The SNM of the
SRAM cell is the length of the diagonal of the maximum square that can fit in the butterfly curve
gate biasing simulations are performed only for LP models which has two
independent gates (front and back gates) while the gates are shorted for SG models.
Figure 2: (a) Schematic diagram for finding SNM [1], (b) Butterfly diagram for finding SNM.
ESULTS AND DISCUSSIONS
The SRAM cell using SG and LP FinFET models are simulated with one of the design parameters
mentioned in the previous section varies while the other parameters are static. For each simulation
run, the subthreshold leakage current, hold SNM, read SNM, read and write delays are measured,
October 2018
3
(1)
2)
transistor. Because the channel length of all
the transistors in the cell are the same, only the width is considered in the ratio. For non-
Subthreshold leakage, hold SNM, read SNM, read delay and write delay are measured by varying
mentioned design parameters while keeping the other design parameters
the results, an optimized SRAM cell with maximum SNM, lower
subthreshold leakage and delay is proposed for both SG and LP 6T FinFET SRAM cells.
coupled inverters
h the noise voltages are
case scenario for noise margins [2].
]. In this method, the voltage
plotted with the inverse of the VTC of the other inverter
transposed onto it. This creates a “butterfly curve”, as seen in Figure 2 (b). The SNM of the
SRAM cell is the length of the diagonal of the maximum square that can fit in the butterfly curve
gate biasing simulations are performed only for LP models which has two
nding SNM.
The SRAM cell using SG and LP FinFET models are simulated with one of the design parameters
mentioned in the previous section varies while the other parameters are static. For each simulation
and write delays are measured,
4. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
4
plotted and analyzed. In the following section we discuss the simulation results and observations
in detail.
3.1. SRAM CELL RATIO VARIATION
The cell ratio is defined as the ratio of the width over length (W/L) of the NFET pull-down
transistors to the NFET access transistors. The cell ratio contributes to the performance and
stability of the SRAM during a read operation. With a larger cell ratio, there is a reduced risk of
data loss during a read operation. This stability has a performance penalty, because of the
negative impact on the read current [5]. Typically, the cell ratio is chosen to be between 1.3-2x to
provide a sufficient ratio for read stability for CMOS designs [3]. Both the SG and LP design
schemes were simulated with a cell ratio of the width of the inverter NFET pair to the width of
the access transistor NFET pair of 1:1 to 8:1.
3.1.1. SUBTHRESHOLD LEAKAGE CURRENT
When varying the cell ratio, the subthreshold leakage current increased with the increase in the
cell ratio which is increased by the size of the NFET transistors, as shown in Figure 3. Compared
to the use of SG transistors, the LP transistor SRAM had two orders less leakage current. The
increase in cell ratio did show the greatest increase in subthreshold leakage current when
increasing the inverters NFET transistors from a 1:1 ratio up to a 2.5:1 ratio. Both schemes show
an increase of over 700% from 1:1 to 8:1. This indicates that the cell ratio needs to be kept around
2.5:1 to maintain adequate cell performance.
Figure 3: Leakage current dependence on Cell ratio variation.
3.1.2. HOLD SNM
By increasing the cell ratio, the hold SNM decreased for both SG and LP models as shown in the
Figure 4. The SRAM cell using LP model had 70 mV lower noise margin compared to the SG
model for a cell ratio of 1:1and the difference remained constant with varying cell ratio. At a ratio
of 2.5:1, the hold SNM was reduced by 2.4% and 3.6% for the SG and LP schemes respectively.
From a ratio of 1:1 to 8:1, the SNM was only reduced by 19.79mV (6.6%) for the SG scheme,
and 21.1mV (9.3%) for the LP scheme. For each cell ratio, the hold SNM of the LP scheme
remained around 75% of the SG scheme’s hold SNM. From a hold SNM perspective, the cell
ratio does not significantly impact the hold noise immunity.
5. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
5
Figure 4: Hold SNM dependence on Cell ratio variation.
3.1.3. READ SNM
By increasing the cell ratio, the ratio of the pull-down current IPD to the access current IACCESS
(β) increases. This increases the noise immunity during read operations [3]. Having a sufficient β
ensures that the inverter storing a logical "0" will not be pulled up to "1" because of the bit-line
pre-charge voltage. For the SG scheme, increasing the cell ratio from 1:1 to 8:1 increased the read
SNM from 140 mV to 178 mV as shown in the Figure 5. However, for the LP scheme, increasing
the cell ratio decreased the read SNM from 177 mV to 169 mV. At a cell ratio of 8:1, the SG
scheme’s read SNM has an increase of 27.3% while the for LP scheme it decreases by 4.3%. For
a cell ratio of 2:1, an 8.5% increase for the SG scheme, and 1.1 % decrease for the LP scheme is
observed for read SNM.
Figure 5: Read SNM dependence on Cell ratio variation.
3.1.4. READ AND WRITE DELAY
The read and write delay of the SG scheme are less severely affected by the cell ratio as shown in
Figure 6 and Figure 7 respectively. The read delay for LP scheme increased with the cell ratio till
it reached a ratio of 3:1, having a read delay of 103.5ps and then it decreases with the increase in
cell ratio. For cell ratio from 1:1 to 8:1, the LP scheme had an overall reduction of the read delay
of 29%, from 72.5ps to 51.5ps. The write delay for SG increased slightly from 2.56ps to 7.5ps.
The shorted-gate scheme shows the most resilience to cell ratio increase in the both delays. The
write delay of the LP scheme increases with increasing cell ratio from 1:1 to 8:1 of 27.9%.
6. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
6
Figure 6: Read delay dependence on Cell ratio variation.
Figure 7: Write delay dependence on Cell ratio variation.
3.2. SRAM PULL-UP RATIO VARIATION
To improve the SNM during writing, the ratio of the width over length (W/L) of the NFET access
transistors to the PFET pull-up transistors can be adjusted. The access transistor must be strong
enough to overcome the PFET when writing a logical "0" to the inverter [5]. Therefore, to
improve the write SNM of the SRAM, one must either increase the width of the access transistors
to increase the drive strength or decrease the width of the PFET transistors. Since reducing the
size of the NFET transistors would inhibit the write SNM, the PFET transistors were adjusted for
testing. The pull-up ratio was varied from 1:1 to 8:1 for all the following simulations to observe
its dependence on the cell characteristics.
3.2.1. SUBTHRESHOLD LEAKAGE CURRENT
Our simulation studies revealed that, increasing the pull-up ratio leads to increase in
subthreshold leakage current as shown in Figure 8. The leakage associated with the LP scheme
remained at 1.35% of the leakage associated with the SG scheme. The leakage current increased
from 2 fA to 6 fA for LP scheme and 102 fA to 170 fA for SG scheme for pull-up ratio from 1:1
to 8:1.
7. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
7
Figure 8: Leakage current dependence on Pull-Up ratio variation.
3.2.2. HOLD SNM
It was observed that, increasing the pull-up ratio has little effect on the hold SNM for the SG
scheme, as seen in Figure 9. The hold SNM increases by just 0.4 mV over the simulation. The LP
scheme has a decrease in hold SNM by 9.8% for the simulation range. The hold SNM of the LP
scheme decreases from 239.85mV to 216.3mV. The simulation results indicate that the pull-up
ratio has little effect on the hold SNM.
Figure 9: Hold SNM dependence on Pull-Up ratio variation.
3.2.3. READ SNM
Increasing the pull-up ratio improves the read SNM, as shown in Figure 10. The LP scheme
shows a greater increase in RSNM compared to the SG scheme. At 2.5:1, the read SNM of the LP
scheme improves by 19.8mV, while the SG improves by 7.1mV. At the maximum ratio, the SG
scheme’s read SNM improved by 10.3%, while the LP scheme’s read SNM improved by 28.5%.
3.2.4. READ AND WRITE DELAY
Figure 11 and Figure 12 shows the read and write delay dependence of the 6T SRAM cell on the
pull-up ratio. Increasing the pull-up ratio of the SRAM cell increases the read delay and write
delay for LP and SG schemes respectively. From a cell ratio of 1:1 to 8:1, the SG scheme had
negligible change in read delay, while for the LP scheme the read delay increased from 86.5ps to
270.5ps. Conversely, the pull-up ratio had almost no effect on the LP scheme write delay, and an
increase in write delay from 2.9ps to 11.3ps for the SG scheme.
8. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
8
Figure 10: Read SNM dependence on Pull-Up ratio variation.
Figure 11: Read delay dependence on Pull-Up ratio variation.
Figure 12: Write delay dependence on Pull-Up ratio variation.
3.3. SUPPLY VOLTAGE VARIATION
3.3.1. SUBTHRESHOLD LEAKAGE CURRENT
The effect of supply voltage on the cell characteristics is investigated by varying it from 0V to
1.2V. The leakage current for SG and LP schemes are plotted in Figure 13. It can be observed that
the leakage current of SG scheme is higher than LP schemes up to 1V. Supply voltage beyond
1V, LP scheme has higher leakage currents. This leakage currents are tabulated in Table 2.
9. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
9
Figure 13: Leakage current dependence on Vdd variation.
Table 2: Supply voltage vs Leakage current
Vdd(V) SG Leakage (fA) LP Leakage (fA)
0.1 53.62 0.004
0.2 61.67 0.051
0.3 70.18 0.466
0.4 79.30 0.835
0.5 89.05 0.951
0.6 99.47 1.12
0.7 110.59 1.67
0.8 122.45 5.30
0.9 135.09 36.28
3.3.2. HOLD SNM
Increasing Vdd improves the hold SNM as shown in Figure 14. For a supply voltage of less than
0.3V, the LP hold SNM remains close to 0V. Below this voltage, SRAM cannot hold the data.
The hold SNM for the SG scheme is linear from 0.1V to 1V. For supply voltage of 1.2V, SG and
LP schemes have a hold SNM of 500mV and 325mV respectively.
Figure 14: Hold SNM dependence on Vdd variation.
10. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
10
3.3.3. READ SNM
Similar to the hold SNM, the read SNM increases with Vdd as plotted in Figure 15. The SG
scheme has a linear increase in read SNM from 0.5V to 1V. For a supply voltage of 1.2V, SG and
LP schemes have a read SNM of 460mV and 325mV respectively.
Figure 15: Read SNM dependence on Vdd variation.
3.3.4. READ AND WRITE DELAY
The read and write delay of the 6T SRAM cell decreased exponentially with increasing voltage as
shown in Figure 16 and Figure 17 respectively. For supply voltage of less than 0.4V, both the SG
and LP had larger read delay as the transistors are operating in subthreshold region and the read
fails for lower supply voltages. Write delay was four orders larger than read delay and the write
failure occurred at a supply voltage of less than 0.5V.
Figure 16: Read delay dependence on Vdd variation.
Figure 17: Write delay dependence on Vdd variation.
11. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
11
3.4. WORD-LINE VOLTAGE VARIATION
The word-line voltage is varied from 0V to 1.2V. The subthreshold leakage current and hold
SNM show no change as the WL voltage should be 0V during hold operation.
3.4.1. READ SNM
Increasing the word line voltage beyond the threshold voltage results in a linear decrease of read
SNM as shown in Figure 18. For the SG scheme, the read SNM starts to decrease from 300 mV
for WL of over 0.25V. For the LP scheme, the read SNM starts to decrease from 165 mV for WL
of over 0.4 V. For WL of 1V and above, the SG and LP schemes have read SNM of 0V.
Figure 18: Read SNM Dependence on Word-Line Voltage variation.
3.4.2. READ AND WRITE DELAY
The read and write delay decreases exponentially for LP schemes as shown in Figure 19 and
Figure 20 respectively. The SG scheme has lower read and write delays. The read and write delay
could not be measured for world line voltages less than 0.8V and 0.6 V respectively. For the word
line voltage of 0.85V to 1.2V, the read delay for the LP scheme reduced by 87.7ps. The read
delay remained constant at 5ps for word line voltage greater than 0.95V. The LP scheme showed
the reduction in write delay from 633.2 ns to 74.1 ps for word line voltage from 0.65V to 1.2V.
Figure 19: Read delay Dependence on Word-Line Voltage variation.
12. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
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3.5. TEMPERATURE VARIATION
3.5.1. SUBTHRESHOLD LEAKAGE CURRENT
Even though it is well known that the leakage current increases with temperature, we investigated
the temperature dependency on subthreshold leakage current by varying the temperature from 0o
C
to 100o
C. Similar to supply voltage variations, the subthreshold leakage current increases
exponentially with the increase in temperature as shown in Figure 21. At higher temperatures, LP
scheme had relatively lower leakage currents. Comparing the leakage currents at 0o
C to leakage
current at room temperature (27o
C), the leakage current increased from 0.11 fA to 10.8 fA and
0.0001fA to 1.48 fA for SG and LP schemes respectively. At 100 o
C the subthreshold leakage
current was 10.04pA and 0.334fA for SG and LP schemes respectively.
Figure 20: Write delay Dependence on Word-Line Voltage variation.
Figure 21: Leakage current Dependence on Temperature Variation.
3.5.2. HOLD SNM
As shown in Figure 22, for temperature from 0o
C to 100o
C, the hold SNM for SG scheme
decreased from 303.5 mV to 292.83 mV and for LP scheme the hold SNM decreased from 232.72
mV to 212.49 mV which corresponds to about 9% decrease in the SNM. At room temperature,
the hold SNM was 300.84mV and 227.66mV for SG and LP schemes respectively.
3.5.3. READ SNM
Simulation results show that the Read SNM has similar trend as hold SNM as shown in Figure
23. For temperature variation from 0o
C to 100o
C, the read SNM for SG scheme decreased from
141.62 mV to 132.75 mV and for LP scheme the read SNM decreased from 114.85 mV to 95.12
13. International Journal of VLSI design & Communication Systems (VLSICS) Vol.9, No.5, October 2018
13
mV. At room temperature, the hold SNM was 139.46 mV and 110.6 mV for SG and LP schemes
respectively.
Figure 22: Hold SNM Dependence on Temperature Variation.
Figure 23: Read SNM Dependence on Temperature Variation.
3.5.4. READ AND WRITE DELAY
Figure 24 and Figure 25 shows the read and write delay plots of the 6T SRAM cell with varying
temperature respectively. With the increase in temperature, the delay decreased. The read delay
for the LP scheme reduces by 68.4ps as the temperature increases from 15o
C to 100o
C and the
read delay of the SG scheme decreased only by 0.92ps. the write delay decreased by 1.46 ps for
temperature variation from 15o
C to 100o
C for SG scheme. The LP scheme shows a linear
reduction of write delay from 81.2 ns to 3.3 ns for increasing temperature from 15o
C to 100o
C.
3.6. PFET BACK-GATE BIASING VARIATION
Only independent gate LP FinFET models were investigated in this research. The SRAM inverter
PFET back-gate voltage is incremented from 0V to 1.2V, while the NFET’s back gate voltage is
held at -0.2V. The results are discussed in the following sections.
3.6.1. SUBTHRESHOLD LEAKAGE CURRENT
The leakage current reduces exponentially as the back-gate bias increases as shown in Figure 26.
For a back-gate biasing of 0V, the leakage current is high at 63.2nA. As the back-gate bias
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decreases to 0.25V, the additional inversion of the channel results in a subthreshold leakage
current of 0.21nA, a reduction of 99.67%.
Figure 24: Read delay dependence on Temperature Variation.
Figure 25: Write delay dependence on Temperature Variation.
Figure 26: Leakage current dependence on PFET Back-Gate biasing.
3.6.2. HOLD SNM AND READ SNM
Figure 27 shows the read and hold SNM for PFET back gate biasing from 0V to 1.2V. for back
gate bias of 0.73V, the maximum hold SNM of 241.2 mV is observed and a maximum read SNM
is 192.94mV is achieved at back-gate bias voltage of 0.57V.
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3.6.3. READ AND WRITE DELAY
Figure 28 shows the read delay plot for varying PFET back-gate bias voltage respectively. As the
back-gate bias of the inverter PFETs increases from 0V to 0.5V, the read delay increases in steps
from 44ps to a maximum delay of 54.7ps which remains constant for back gate bias voltage
greater than 0.5V.Figure 29 gives write delay plot for varying PFET back-gate bias voltage. The
write delay increases exponentially with the increase of PFET back-gate bias after 0.77V. At this
voltage, the write delay increases from 8.7ns to 41.1ns. Write delay is four orders larger than read
delay.
Figure 27: Read and Hold SNM dependence on PFET Back-Gate biasing.
3.7. NFET BACK-GATE BIASING VARIATION
The back-gate bias for the SRAM inverter NFET transistors is simulated for a back-gate voltage
from -0.65V to 0V, while the PFETs back-gate bias voltage is held at Vdd+0.2V (0.88V) for the
LP scheme. The results are discussed in detail below.
3.7.1. HOLD SNM AND READ SNM
The hold SNM shows a greater increase for increasing back-gate bias with a maximum of
269.7mV at a back-gate bias of -0.03V. The read SNM peaks at 175.93mV at -0.2V. The read and
hold SNM is plotted in Figure 30.
Figure 28: Read delay dependence on PFET Back-Gate biasing.
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3.7.2. SUBTHRESHOLD LEAKAGE CURRENT
When varying the NFET back-gate biases, the leakage current reduces exponentially as the back-
gate bias decreases, as shown in Figure 31. For a back-gate bias of 0V, the leakage current is
84.7fA. For a back-gate bias of -0.25V, the additional inversion of the channel results in a
subthreshold leakage current of 3.25fA. For the back-gate bias that has the highest read SNM (-
0.2V), the subthreshold leakage current is 4.16fA. For the back-gate voltage that produced the
best hold SNM (-0.03V), the subthreshold leakage is 35.98fA. Unlike the PFET back-gate
biasing, the NFET back-gate biasing that produced the best read and hold SNM did not reduce the
leakage current when compared to the subthreshold leakage current for a back-gate bias of 0V.
However, the subthreshold leakage current reduction is minimal when back-gate is biased beyond
-0.2V.
Figure 29: Write delay dependence on PFET Back-Gate biasing.
Figure 30: SNM dependence on NFET Back-Gate biasing.
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Figure 31: Leakage current dependence on NFET Back-Gate biasing.
3.7.3. READ AND WRITE DELAY
It was observed that back-gate biasing the SRAM cell’s NFET transistors from -0.68V to 0V,
both the read and write delay decreased exponentially as shown in Figure 32 and Figure 33
respectively. Increasing the back-gate bias voltage from -0.24 to 0V reduces the read delay from
85.8ps to 8.7ps. Similarly, reducing the back-gate bias from -0.68V to -0.24V reduces the write
delay from 555.3ps to 155.3ps. Further reduction from -0.24V to 0V reduces the write delay to
95.3ps.
Figure 32: Read delay dependence on NFET Back-Gate biasing.
Figure 33: Write delay dependence on NFET Back-Gate biasing.
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4. OPTIMIZED DESIGN PARAMETERS
By carefully analyzing the simulation results, optimum parameters for both the SG and LP
FinFET SRAM cells were selected. Table 3 shows the chosen design metrics for SG and LP
design schemes for optimum SNM and power-performance in comparison to the original design
metrics. Using these new design metrics for both SG and LP, 6T FinFET SRAM cells are
simulated and analyzed. The cell ratio of 2:1, which was selected based on the original
simulation, increases the read margins. Increasing the NFET dimensions improves the read SNM
for SG scheme while it slightly decreases the hold SNM and increases leakage currents. The pull-
up ratio increases the write margin and the pull-up ratio of 2:1 is selected which marginally
decreases hold SNM and increases the leakage currents. Increasing the supply voltage increases
both read and hold SNM but at the cost of increased subthreshold leakage currents. A supply
voltage of 0.6V is chosen which increases the SNMs while minimizing the increase in the leakage
currents. The word-line voltage of 0.6V is chosen which only increased the read SNM. PFET
back-gate bias selected was 0.6V and NET back-gate bias was -0.2V which provided a balanced
tradeoff between stability and subthreshold leakage currents. This back-gate bias combination in
LP scheme increased the read SNM as compared to the SG scheme. The simulation results using
the new design metrics are tabulated in Table 4 and Table 5 for SG and LP schemes respectively
which is compared with the original design metric simulation results.
Table 3: Simulation Design Metrics
Design Parameter Original Optimized
Cell Ratio 1:1 2:1
Pull-Up Ratio 1:1 2:1
Supply Voltage (V) 0.68 0.6
Word Line Voltage (V) 0.68 0.8
PFET Bias (V) 0.88 0.6
NFET Bias (V) -0.2 -0.2
Table 4:Shorted Gate Design Metrics Results
Scheme SG SG Modified Percentage increase (%)
Subthreshold Leakage (pA) 0.1803 0.2372 31.55
Hold SNM (mV) 300.84 388.52 29.14
Read SNM (mV) 139.46 401.02 187.55
Read Delay (ps) 2.782 79.99 2775.2
Write Delay (ns) 1.6 4.014 150.87
Read Energy (fJ) 1.04E-5 5.08E-6 -51.11
Write Energy (fJ) 3.71E-5 7.62E-5 105.39
Average Energy (fJ) 1.57E-5 1.93E-5 22.92
Table 5: Low Power Design Metrics Results
Scheme LP LP Modified Percentage increase (%)
Subthreshold Leakage (pA) 0.001485 0.02633 1673.06
Hold SNM (mV) 279.62 256.78 -8.16
Read SNM (mV) 110.6 256.81 132.19
Read Delay (ps) 1152.1 1300 12.83
Write Delay (ns) 107.58 602.39 459.94
Read Energy (fJ) 7.78E-7 1.73E-6 122.36
Write Energy (fJ) 1.03E-6 2.01E-6 95.14
Average Energy (fJ) 8.28E-7 1.79E-6 116.18
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5. CONCLUSIONS
Seven design metrics of 6T FinFET SRAM cells were investigated and their effects on the SNM
and the performance for a SG and LP design schemes were analyzed. Based on the simulation
results, a 6T FinFET SRAM cell with new design metrics is proposed with optimum SNM and
performance tradeoffs.
5.1. SHORTED-GATE DESIGN
Table 4 shows the results for the modified SG configuration. The hold SNM improved by 29.14%
and the read SNM improved by 187.55%. This increased noise immunity comes at the cost of
both subthreshold leakage and read and write delays. The average energy for read and writes of
the modified cell increased by 22.92% when compared with the original cell.
5.2. LOW POWER LP DESIGN
Table 5 shows the results for the modified LP configuration. The hold SNM decreased by 8.16%
margin, while the read SNM improved by 132.19% margin. This increased noise immunity comes
at the cost of both subthreshold leakage and read and write delays. The subthreshold leakage
current increased by 25fA, the read delay by 12.8%, and the write delay by 459.9%. The average
energy for read and writes of the modified cell is 116.18% when compared with the original cell.
REFERENCES
[1] B. Dipert. Fundamentals Of Volatile Memory Technologies. Electronic Products, 2011.
[2] A. Pavlov. Cmos Sram Circuit Design And Parametric Test In Nano-Scaled Technologies, Volume
40 Of Frontiers In Electronic Testing, Springer Berlin- Heidelberg, 2008.
[3] J. Colinge. Finfets And Other Multi-Gate Transistors. Springer Berlin- Heidelberg, 2008.
[4] J. Rabaey. Low Power Design Essentials. Springer New York, 2009.
[5] D. Payne. Designing With Finfets, Oct. 2012. Http://Www.Soi.Tec.Ufl.Edu/Ufdg.Html.
[6] University Of Florida. Process/Physics-Based Generic Double-Gate Mosfet Model, Oct 2011.
[7] C. Hill. Definitions Of Noise Margin In Logic Systems. Mullard Technology Communications,
Pages 239–245, Sept 1967.
[8] E. S. Et Al. Static-Noise Margin Analysis Of Mos Sram Cells. Ieee Journal Of Solid State Circuits,
Sc-22(5):748–754, 1987.
[9] B. H. Et. Al. Calhoun. Analyzing Static Noise Margin For Sub-Threshold Sram In 65nm Cmos.
Proc. Esscirc, Pages 363–366, Sept 2005.
[10] S. Koppa And E. B. John “Performance Tradeoffs In The Design Of Low-Power Sram Arrays For
Implantable Devices”, Journal Of Low Power Electronics, Vol 14, No. 1, March 2018.
[11] Monica M And P. Chandramohan, "Characterization Of 8t Sram Cells Using 16 Nm Finfet
Technology," 2016 International Conference On Signal Processing And Communication (Icsc),
Noida, 2016, Pp. 403-406.
[12] T. S. Copetti, T. R. Balen, G. C. Medeiros And L. M. B. Poehls, "Analyzing The Behavior Of Finfet
Srams With Resistive Defects," 2017 Ifip/Ieee International Conference On Very Large Scale
Integration (Vlsi-Soc), Abu Dhabi, 2017, Pp. 1-6.
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AUTHORS
DustenVernorreceived his B.S. and M.Sdegrees, both in Computer Engineering, from the
University of Texas at San Antonio. Currently he is an engineer at Broadcom Inc. in Austin,
Texas. His research interests include, Efficient VLSI System Design and Low Power VLSI
Systems.
Santosh Koppa received his Ph. D. in electrical engineering from the University of Texas at San
Antonio.He is currently working in Global Foundries US Inc. His research interests include, ultra-
low power analog and digital circuits,Internet of Things, low power VLSI systems.
Eugene John received his Ph. D. in electrical engineering from the Pennsylvanian State
University. He is currently a professor in the department of electrical and computer engineering.
His research interests include, ultra-low power computing, energy efficient hardware for deep
learning, low power VLSI systems, integrated circuit IP security and trust and computer
architecture and performance evaluation.