Skip to main content
SpringerLink
Log in

Thermally-aware circuit model and performance analysis of MLGNR for nano-interconnect application

  • Published:
Analog Integrated Circuits and Signal Processing Aims and scope Submit manuscript

Abstract

This paper explores the influence of temperature on the scattering mechanism of multilayer graphene nanoribbon (MLGNR). A thermally aware electrical ESC model along with mathematical computations is presented for evaluating the parasitic and reports the performance analysis dependent on temperature of the MLGNR at global interconnect length for 16 nm, 22 nm, and 32 nm nodes of technology in terms of power dissipation, delay, and power delay product (PDP). It was examined that with rising temperature, there is a strident decrease in the mean free path of GNR interconnect, which further influence its own resistance at variable global lengths (500‒2000 μm) for all three technology nodes. The simulation program with integrated circuit (SPICE) emphasis simulation tool is used to estimate and compare the performance of MLGNR in terms of power dissipation, signal delay and PDP for three different nodes of technology. It is revealed from the outcomes that the propagation delay and PDP increase at long interconnects (2000 μm) over a temperature range of 200 to 500 K for deep submicron technology nodes (16, 22, and 32 nm). Further, based on ITRS 2013, the analytical and simulated results are obtained at global interconnect length (2000 μm) for 16 nm technology node in the 200–500 K temperature range of MLGNR. The simulation and analytical results show that the outcomes of the two models are very similar. The models' trends show an increase in delay with increasing temperature levels (200‒500 K) 16 nm technology node.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Meindl, J. D. (2003). Interconnect opportunities for gigascale integration. IEEE Micro, 23(3), 28–35. https://doi.org/10.1109/MM.2003.1209464

    Article  Google Scholar 

  2. Sarto, M. S., Tamburrano, A., & D’Amore, M. (2008). New electron-waveguide-based modeling for carbon nanotube interconnects. IEEE Transactions on Nanotechnology, 8(2), 214–225. https://doi.org/10.1109/TNANO.2008.2010253

    Article  ADS  Google Scholar 

  3. Pop, E., Mann, D., Wang, Q., Goodson, K., & Dai, H. (2006). Thermal conductance of an individual single-wall carbon nanotube above room temperature. Nano letters, 6(1), 96–100. https://doi.org/10.1021/nl052145f

    Article  ADS  CAS  PubMed  Google Scholar 

  4. Fang, Y., Zhao, W. S., Wang, X., Jiang, F., & Yin, W. Y. (2012, May). Circuit modelling of multilayer graphene nanoribbon (MLGNR) interconnects. In 2012 Asia-Pacific symposium on electromagnetic compatibility (pp. 625‒628). IEEE. https://doi.org/10.1109/APEMC.2012.6237942.

  5. Hosseini, A., & Shabro, V. (2010). Thermally-aware modeling and performance evaluation for single-walled carbon nanotube-based interconnects for future high performance integrated circuits. Microelectronic Engineering, 87(10), 1955–1962. https://doi.org/10.1016/j.mee.2009.12.008

    Article  CAS  Google Scholar 

  6. Li, B., Sullivan, T. D., Lee, T. C., & Badami, D. (2004). Reliability challenges for copper interconnects. Microelectronics Reliability, 44(3), 365–380. https://doi.org/10.1016/j.microrel.2003.11.004

    Article  CAS  Google Scholar 

  7. Li, H., Xu, C., & Banerjee, K. (2010). Carbon nanomaterials: The ideal interconnect technology for next-generation ICs. IEEE Design & Test of Computers, 27(4), 20–31. https://doi.org/10.1109/MDT.2010.55

    Article  Google Scholar 

  8. Balandin, A. A., Ghosh, S., Bao, W., Calizo, I., Teweldebrhan, D., Miao, F., & Lau, C. N. (2008). Superior thermal conductivity of single-layer graphene. Nano Letters, 8(3), 902–907. https://doi.org/10.1021/nl0731872

    Article  ADS  CAS  PubMed  Google Scholar 

  9. Naeemi, A., & Meindl, J. D. (2007). Conductance modeling for graphene nanoribbon (GNR) interconnects. IEEE Electron Device Letters, 28(5), 428–431. https://doi.org/10.1109/LED.2007.895452

    Article  ADS  CAS  Google Scholar 

  10. Li, H., Xu, C., Srivastava, N., & Banerjee, K. (2009). Carbon nanomaterials for next-generation interconnects and passives: Physics, status, and prospects. IEEE Transactions on Electron Devices, 56(9), 1799–1821. https://doi.org/10.1109/TED.2009.2026524

    Article  ADS  CAS  Google Scholar 

  11. Sharma, H., & Sandha, K. S. (2018). Multilayer graphene nanoribbon (MLGNR) as VLSI interconnect material at nano-scaled technology nodes. Transactions on Electrical and Electronic Materials, 19(6), 456–461. https://doi.org/10.1007/s42341-018-0070-4

    Article  Google Scholar 

  12. Cavin, R. K., Zhirnov, V. V., Herr, D. J. C., Avila, A., & Hutchby, J. (2006). Research directions and challenges in nanoelectronics. Journal of Nanoparticle Research, 8(6), 841–858. https://doi.org/10.1007/s11051-006-9123-4

    Article  ADS  Google Scholar 

  13. Sharma, H., & Sandha, K. S. (2020). Impact of intercalation doping on the conductivity of multi-layer graphene nanoribbon (MLGNR) in on-chip interconnects. Journal of Circuits, Systems and Computers, 29(12), 2050185. https://doi.org/10.1142/S0218126620501856

    Article  Google Scholar 

  14. Kumar, V. R., Majumder, M. K., Kukkam, N. R., & Kaushik, B. K. (2015). Time and frequency domain analysis of MLGNR interconnects. IEEE Transactions on Nanotechnology, 14(3), 484–492. https://doi.org/10.1109/TNANO.2015.2408353

    Article  ADS  CAS  Google Scholar 

  15. Han, M. Y., Özyilmaz, B., Zhang, Y., & Kim, P. (2007). Energy band-gap engineering of graphene nanoribbons. Physical Review Letters, 98(20), 206805. https://doi.org/10.1103/PhysRevLett.98.206805

    Article  ADS  CAS  PubMed  Google Scholar 

  16. Gunlycke, D., Lawler, H. M., & White, C. T. (2007). Room-temperature ballistic transport in narrow graphene strips. Physical Review B, 75(8), 085418. https://doi.org/10.1103/PhysRevB.75.085418

    Article  ADS  CAS  Google Scholar 

  17. Naeemi, A., & Meindl, J. D. (2009). Compact physics-based circuit models for graphene nanoribbon interconnects. IEEE Transactions on Electron Devices, 56(9), 1822–1833. https://doi.org/10.1109/TED.2009.2026122

    Article  ADS  CAS  Google Scholar 

  18. Berger, C., Song, Z., Li, X., Wu, X., Brown, N., Naud, C., ...& de Heer, W. A. (2006). Electronic confinement and coherence in patterned epitaxial grapheme. Science, 312(5777), 1191–1196. https://doi.org/10.1126/science.11259

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Perebeinos, V., & Avouris, P. (2010). Inelastic scattering and current saturation in graphene. Physical Review B, 81(19), 195442. https://doi.org/10.1103/PhysRevB.81.195442

    Article  ADS  CAS  Google Scholar 

  20. Xu, C., Li, H., & Banerjee, K. (2009). Modeling, analysis, and design of graphene nano-ribbon interconnects. IEEE Transactions on Electron Devices, 56(8), 1567–1578. https://doi.org/10.1109/TED.2009.2024254

    Article  ADS  CAS  Google Scholar 

  21. Sharma, H., & Sandha, K. S. (2020). Investigation on the combined effects of variable Fermi energies and temperatures on the performance of multilayer graphene nanoribbon as interconnects. Analog Integrated Circuits and Signal Processing, 104, 157–168. https://doi.org/10.1007/s10470-020-01681-2

    Article  Google Scholar 

  22. Cui, J. P., Zhao, W. S., Yin, W. Y., & Hu, J. (2011). Signal transmission analysis of multilayer graphene nano-ribbon (MLGNR) interconnects. IEEE Transactions on Electromagnetic Compatibility, 54(1), 126–132. https://doi.org/10.1109/TEMC.2011.2172947

    Article  Google Scholar 

  23. International Technology Roadmap for Semiconductors, 2013 Edition, http://public.itrs.net. Accessed 01 September, 2022

  24. Predictive Technology Models. [Online]. http://ptm.asu.edu

  25. Nishad, A. K., & Sharma, R. (2013). Analytical time-domain models for performance optimization of multilayer GNR interconnects. IEEE Journal of Selected Topics in Quantum Electronics, 20(1), 17–24. https://doi.org/10.1109/JSTQE.2013.2272458

    Article  ADS  CAS  Google Scholar 

  26. Kumar, V., & Naeemi, A. (2012). Analytical models for the frequency response of multi-layer graphene nanoribbon interconnects. In 2012 IEEE international symposium on electromagnetic compatibility (pp. 440‒445). IEEE. https://doi.org/10.1109/ISEMC.2012.6351837.

  27. Kumar, V. R., Majumder, M. K., Alam, A., Kukkam, N. R., & Kaushik, B. K. (2015). Stability and delay analysis of multi-layered GNR and multi-walled CNT interconnects. Journal of Computational Electronics, 14, 611–618. https://doi.org/10.1007/s10825-015-0691-3

    Article  CAS  Google Scholar 

  28. Majumder, M. K., Kukkam, N. R., & Kaushik, B. K. (2014). Frequency response and bandwidth analysis of multi-layer graphene nanoribbon and multi-walled carbon nanotube interconnects. Micro & Nano Letters, 9(9), 557–560. https://doi.org/10.1049/mnl.2013.0742

    Article  Google Scholar 

  29. Kumar, V. R., Kaushik, B. K., & Patnaik, A. (2015). Crosstalk noise modeling of multiwall carbon nanotube (MWCNT) interconnects using finite-difference time-domain (FDTD) technique. Microelectronics Reliability, 55(1), 155–163. https://doi.org/10.1016/j.microrel.2014.09.001

    Article  CAS  Google Scholar 

  30. Liang, F., Wang, G., & Ding, W. (2011). Estimation of time delay and repeater insertion in multiwall carbon nanotube interconnects. IEEE Transactions on Electron Devices, 58(8), 2712–2720. https://doi.org/10.1109/TED.2011.2154334

    Article  ADS  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Chandigarh University, Mohali, 140413, India

    Himanshu Sharma

  2. Department of ECE, Thapar Institute of Engineering and Technology, Patiala, 147004, India

    Karmjit Singh Sandha

Authors
  1. Himanshu Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karmjit Singh Sandha
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Section 1 and 2 is the collaborative work of both the authors. Section 3 and 4 are written by 1st and 2nd author respectively Figures are prepared by author 1. All authors reviewed the manuscript

Corresponding author

Correspondence to Himanshu Sharma.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, H., Sandha, K.S. Thermally-aware circuit model and performance analysis of MLGNR for nano-interconnect application. Analog Integr Circ Sig Process (2024). https://doi.org/10.1007/s10470-024-02254-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10470-024-02254-3

Keywords

Search

Navigation