A Study on the Design Method of Hybrid MOSFET-CNTFET Based SRAM – A Secondary Publication
Download PDF

Keywords

MOSFET
CNTFET
SRAM
Hybrid
Carbon nanotube

DOI

10.26689/jera.v8i1.6115

Submitted : 2024-01-21
Accepted : 2024-02-05
Published : 2024-02-20

Abstract

More than 10,000 carbon nanotube field-effect transistors (CNTFETs) have been successfully integratedinto one semiconductor chip using conventional semiconductor design procedures and manufacturing processes. Thesetransistors offer advantages such as high carrier mobility, large saturation velocity, low intrinsic capacitance, flexibility, andtransparency. The three-dimensional multilayer structure of the CNTFET semiconductor chip, along with ongoing researchin CNTFET manufacturing processes, increases the potential for creating a hybrid MOSFET-CNTFET semiconductorchip. This chip combines conventional metal-oxide-semiconductor field-effect transistors (MOSFETs) and CNTFETs inone integrated system. This paper discusses a methodology to design 6T binary static random-access memory (SRAM)using a hybrid MOSFET-CNTFET. This paper introduces a method for designing a hybrid MOSFET-CNTFET SRAMby leveraging existing MOSFET SRAM or CNTFET SRAM design approaches. Additionally, this paper compares itsperformance with conventional MOSFET SRAM and CNTFET SRAM designs.

References

Franklin AD, Hersam MC, Wong H-SP, 2022, Carbon Nanotube Transistors: Making Electronics from Molecules. Science, 378(6621): 726–732. https://doi.org/10.1126/science.abp8278

Zhang S, Pang J, Li Y, et al., 2022, Emerging Internet of Things Driven Carbon Nanotubes-Based Devices. Nano Research, 15: 4613–4637. https://doi.org/10.1007/s12274-021-3986-7

Chen R, Chen L, Liang J, et al., 2022, Carbon Nanotube SRAM in 5-nm Technology Node Design, Optimization, and Performance Evaluation – Part I: CNFET Transistor Optimization. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 30(4): 432–439. https://doi.org/10.1109/TVLSI.2022.3146125

Chen R, Chen L, Liang J, et al., 2022, Carbon Nanotube SRAM in 5-nm Technology Node Design, Optimization, and Performance Evaluation – Part II: CNT Interconnect Optimization. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 30(4): 440–448. https://doi.org/10.1109/TVLSI.2022.3146064

Deng J, Wong H-SP, 2007, A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application – Part I: Model of the Intrinsic Channel Region. IEEE Transactions on Electron Devices, 54(12): 3186–3194. https://doi.org/10.1109/TED.2007.909030

Deng J, Wong H-SP, 2007, A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application – Part II: Full Device Model and Circuit Performance Benchmarking. IEEE Transactions on Electron Devices, 54(12): 3195–3205. https://doi.org/10.1109/TED.2007.909043

Yang D, Hwang H, Kim Y-J, et al., 2023, High-Performance Carbon Nanotube Field-Effect Transistors with Electron Mobility of 39.4 cm2V?1s?1 Using Anion–? Interaction Doping. Carbon, 203: 761–769. https://doi.org/10.1016/j.carbon.2022.12.025

Zhou J, Ren L, Li H, et al., 2023, Carbon Nanotube Radiofrequency Transistors With fT/fMAX of 376/318 GHz. IEEE Electron Device Letters, 44(2): 329–332. https://doi.org/10.1109/LED.2022.3227133

Liu C, Cao Y, Wang B, et al., 2022, Complementary Transistors Based on Aligned Semiconducting Carbon Nanotube Arrays. ACS Nano, 16(12): 21482–21490. https://doi.org/10.1021/acsnano.2c10007

Long G, Jin W, Xia F, et al., 2022, Carbon Nanotube-Based Flexible High-Speed Circuits with Sub-Nanosecond Stage Delays. Nature Communications, 13(1): 6734. https://doi.org/10.1038/s41467-022-34621-x

Shin D-H, You YG, Jo SI, et al., 2022, Low-Power Complementary Inverter Based on Graphene/Carbon-Nanotube and Graphene/MoS2 Barristors. Nanomaterials, 12(21): 3820.https://doi.org/10.3390/nano12213820

Huang Q, Wang J, Li C, et al., 2022, Intrinsically Flexible All Carbon-Nanotube Electronics Enabled By a Hybrid Organic-Inorganic Gate Dielectric. npj Flexible Electron, 6: 61.https://doi.org/10.1038/s41528-022-00190-8

Hills G, Lau C, Wright A, et al., 2019, Modern Microprocessor Built From Complementary Carbon Nanotube Transistors. Nature, 572: 595–602. https://doi.org/10.1038/s41586-019-1493-8

Singh RS, Takagi K, Aoki T, et al., 2022, Precise Deposition of Carbon Nanotube Bundles by Inkjet-Printing on a CMOS-Compatible Platform. Materials, 15(14): 4935. https://doi.org/10.3390/ma15144935

Valluri A, Musala S, 2024, Low Leakage, Differential Read Scheme CNTFET Based 9T SRAM Cells for Low Power Applications. International Journal of Electronics, 111(1): 127–148.https://doi.org/10.1080/00207217.2022.2148289

Bakhtiary V, Amirany A, Moaiyeri MH, et al., 2023, An SEU-Hardened Ternary SRAM Design Based on Efficient Ternary C-Elements Using CNTFET Technology. Microelectronics Reliability, 140: 114881. https://doi.org/10.1016/j.microrel.2022.114881

Sachdeva A, Kumar D, Abbasian E, 2023, A Carbon Nano-Tube Field Effect Transistor Based Stable, Low-Power 8T Static Random Access Memory Cell with Improved Write Access Time. AEU – International Journal of Electronics and Communications, 162: 154565. https://doi.org/10.1016/j.aeue.2023.154565

Darabi A, Salehi MR, Abiri E, 2023, One-Sided 10T Static-Random Access Memory Cell for Energy-Efficient and Noise-Immune Internet of Things Applications. International Journal of Circuit and Theory Applications, 51(1): 379–397. https://doi.org/10.1002/cta.3408

Elangovan M, Muthukrishnan M, 2022, Design of High Stability and Low Power 7T SRAM Cell in 32-NM CNTFET Technology. Journal of Circuits, Systems and Computers, 31(13): 2250233.https://doi.org/10.1142/S0218126622502334

Bastani NH, Navi K, 2022, A Low-Power and Robust Quaternary SRAM Cell for Nanoelectronics. Analog Integrated Circuits and Signal Processing, 111: 483–493. https://doi.org/10.1007/s10470-022-02031-0

Cho G, 2021, A Study on the Circuit Design Method of CNTFET SRAM Considering Carbon Nanotube Density. Journal of IKEEE, 25(3): 473–478. https://doi.org/10.7471/ikeee.2021.25.3.473

PTM Models, viewed February 1, 2023. http://ptm.asu.edu/

CNFET Models, viewed February 1, 2023. https://nano.stanford.edu/downloads/stanford-cnfet-model/stanfordcnfetmodel-hspice