Skip to main content
Log in

Implementation and Performance Evaluation of Ferroelectric Negative Capacitance FET

  • Original Paper
  • Published:
Silicon Aims and scope Submit manuscript

Abstract

With the constant increase in power dissipation of nanoscale transistors, the almost four-decade-old cycle of performance advancement in complementary metal–oxide–semiconductor (CMOS) technology is in danger of being disrupted. As revealed in the first study, negative capacitance states in an isolated ferroelectric capacitor can be identified almost instantly when the capacitor is switched on. Increasing ferroelectric volume fraction depolarization, as demonstrated by phase-field modelling, results in rapid expansion of domain walls, which results in a negative capacitance signature. One must understand how the ferroelectric material is connected to the interfacial oxide and semiconductor, as well as how negative capacitance values can be achieved, in order to obtain amplification and margin of error. If one can adhere to these guidelines, your design will be optimized and free of hysteresis issues. The negative capacitance effect of ferroelectric oxides, according to our research, can be leveraged to drastically minimize power dissipation in nanoscale semiconductor transistors. The SS can be reduced to less than 60 mV/dec by using FETs with negative capacitance, such as FE-FETs and other comparable devices. These FETs' gate dielectric is comprised of an unstable substance, making them unstable.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

Data Availability

No supplementary materials.

References

  1. Salamin S, Rapp M, Henkel J, Gerstlauer A, Amrouch H (2020) Dynamic Power and Energy Management for NCFET-Based Processors. IEEE Trans Comput-Aided Des Integr Circuits Syst 39(11):3361–3372. https://doi.org/10.1109/TCAD.2020.3012644

    Article  Google Scholar 

  2. Salamin S et al (2021) Power-Efficient Heterogeneous Many-Core Design With NCFET Technology. IEEE Trans Comput 70(9):1484–1497. https://doi.org/10.1109/TC.2020.3013567

    Article  Google Scholar 

  3. Yuan ZC et al (2019) Toward Microwave S- and X-Parameter Approaches for the Characterization of Ferroelectrics for Applications in FeFETs and NCFETs. IEEE Trans Electron Devices 66(4):2028–2035. https://doi.org/10.1109/TED.2019.2901668

    Article  CAS  Google Scholar 

  4. Agarwal H et al (2018) NCFET Design Considering Maximum Interface Electric Field. IEEE Electron Device Lett 39(8):1254–1257. https://doi.org/10.1109/LED.2018.2849508

    Article  CAS  Google Scholar 

  5. Cam T et al (2020) Sustained Benefits of NCFETs Under Extreme Scaling to the End of the IRDS. IEEE Trans Electron Devices 67(9):3843–3851. https://doi.org/10.1109/TED.2020.3007398

    Article  CAS  Google Scholar 

  6. Paim G et al (2021) On the Resiliency of NCFET Circuits Against Voltage Over-Scaling. IEEE Trans Circuits Syst I Regul Pap 68(4):1481–1492. https://doi.org/10.1109/TCSI.2021.3058451

    Article  Google Scholar 

  7. Liang Y, Li X, Gupta SK, Datta S, Narayanan V (2018) Analysis of DIBL Effect and Negative Resistance Performance for NCFET Based on a Compact SPICE Model. IEEE Trans Electron Devices 65(12):5525–5529. https://doi.org/10.1109/TED.2018.2875661

    Article  CAS  Google Scholar 

  8. Amrouch H, Salamin S, Pahwa G, Gaidhane AD, Henkel J, Chauhan YS (2019) Unveiling the Impact of IR-Drop on Performance Gain in NCFET-Based Processors. IEEE Trans Electron Devices 66(7):3215–3223. https://doi.org/10.1109/TED.2019.2916494

    Article  CAS  Google Scholar 

  9. Gaidhane AD, Pahwa G, Verma A, Chauhan YS (2018) Compact Modeling of Drain Current, Charges, and Capacitances in Long-Channel Gate-All-Around Negative Capacitance MFIS Transistor. IEEE Trans Electron Devices 65(5):2024–2032. https://doi.org/10.1109/TED.2018.2813059

    Article  CAS  Google Scholar 

  10. Li X et al (2017) Advancing Nonvolatile Computing With Nonvolatile NCFET Latches and Flip-Flops. IEEE Trans Circuits Syst I Regul Pap 64(11):2907–2919. https://doi.org/10.1109/TCSI.2017.2702741

    Article  Google Scholar 

  11. Kao M-Y et al (2019) Optimization of NCFET by Matching Dielectric and Ferroelectric Nonuniformly Along the Channel. IEEE Electron Device Lett 40(5):822–825. https://doi.org/10.1109/LED.2019.2906314

    Article  CAS  Google Scholar 

  12. Vega RA, Ando T, Philip TM (2021) Junction Design and Complementary Capacitance Matching for NCFET CMOS Logic. IEEE J Electron Devices Soc 9:691–703. https://doi.org/10.1109/JEDS.2021.3095923

    Article  CAS  Google Scholar 

  13. He G-Y et al (2021) Simultaneous Analysis of Multi-Variables Effect on the Performance of Multi-Domain MFIS Negative Capacitance Field-Effect Transistors. IEEE J Electron Devices Soc 9:741–747. https://doi.org/10.1109/JEDS.2021.3103516

    Article  CAS  Google Scholar 

  14. Zervakis G, Anagnostopoulos I, Salamin S, Chauhan YS, Henkel J, Amrouch H (2021) Impact of NCFET on Neural Network Accelerators. IEEE Access 9:43748–43758. https://doi.org/10.1109/ACCESS.2021.3066335

    Article  Google Scholar 

  15. Salamin S, Zervakis G, Chauhan YS, Henkel J, Amrouch H (2021) PROTON: Post-Synthesis Ferroelectric Thickness Optimization for NCFET Circuits. IEEE Trans Circuits Syst I Regul Pap 68(10):4299–4309. https://doi.org/10.1109/TCSI.2021.3103860

    Article  Google Scholar 

  16. Amrouch H, Pahwa G, Gaidhane AD, Henkel J, Chauhan YS (2018) ‘Negative capacitance transistor to address the fundamental limitations in technology scaling: Processor performance.’ IEEE Access 6:52754–52765

  17. Song J, Cho Y, Park JS, Jang JW, Lee S, Song JH, Lee JG, and Kang I (2019) 7.1 an 11.5TOPS/W 1024-MAC butterfly structure dual-core sparsity-aware neural processing unit in 8nm flagship mobile SoC. In 2019 IEEE International Solid-State Circuits Conference-(ISSCC) (pp. 130-132). IEEE

  18. Jouppi NP, Young C, Patil N, Patterson D, Agrawal G, Bajwa R, Bates S, Bhatia S, Boden N, Borchers A, and Boyle R (2017) In-data center performance analysis of a tensor processing unit. In Proceedings of the 44th annual international symposium on computer architecture (pp. 1-12).

  19. Sandler M, Howard A, Zhu M, Zhmoginov A, and Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520).

  20. Zhang X, Zhou X, Lin M, and Sun J (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6848-6856)

  21. Tan M, Chen B, Pang R, Vasudevan V, Sandler M, Howard A, and Le QV (2019) Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2820-2828)

  22. Yang J, Shen X, Xing J, Tian X, Li H, Deng B, Huang J, and Hua XS (2019) Quantization networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7308-7316).

  23. Gysel P, Pimentel J, Motamedi M, Ghiasi S (2018) ‘Ristretto: A framework for empirical study of resource-efficient inference in convolutional neural networks.’ IEEE Trans Neural Netw Learn Syst 29(11):5784–5789

  24. Amrouch H, Salamin S, Pahwa G, Gaidhane AD, Henkel J, Chauhan YS (2019) ‘Unveiling the impact of IR-drop on performance gain in NCFET-based processors.’ IEEE Trans Electron Devices 66(7):3215–3223

  25. Jacob B, Kligys S, Chen B, Zhu M, Tang M, Howard A, Adam H, and Kalenichenko D (2018) Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2704-2713).

  26. Tasoulas Z-G, Zervakis G, Anagnostopoulos I, Amrouch H, Henkel J (2020) ‘Weight-oriented approximation for energy-efficient neural network inference accelerators.’ IEEE Trans Circuits Syst I Reg Papers 67(12):4670–4683

  27. Amrouch H, Zervakis G, Salamin S, Kattan H, Anagnostopoulos I, Henkel J (2020) ‘NPU thermal management.’ IEEE Trans Comput Aided Design Integr Circuits Syst 39(11):3842–3855

  28. Amrouch H, Pahwa G, Gaidhane AD, Dabhi CK, Klemme F, Prakash O, Chauhan YS (2020) ‘Impact of variability on processor performance in negative capacitance FinFET technology.’ IEEE Trans Circuits Syst I Reg Papers 67(9):3127–3137

  29. Prakash MD, Nelam BG, Ahmadsaidulu S, Navaneetha A, Panigrahy AK (2021) Performance Analysis of Ion-Sensitive Field Effect Transistor with Various Oxide Materials for Biomedical Applications. SILICON. https://doi.org/10.1007/s12633-021-01413-9

  30. Prakash MD, Krsihna BV, Satyanarayana BVV, Vignesh NA, Panigrahy AK, Ahmadsaidulu S (2021) A Study of an Ultrasensitive Label Free Silicon Nanowire FET Biosensor for Cardiac Troponin I Detection. SILICON. https://doi.org/10.1007/s12633-021-01352-5

  31. Meriga C, Ponnuri RT, Satyanarayana BVV, Gudivada A, Panigrahy AK, and Prakash MD (2021) A Novel Teeth Junction Less Gate All Around FET for Improving Electrical Characteristics. Silicon, pp.1-6.. https://doi.org/10.1007/s12633-021-00983-y

  32. Prakash MD, Nihal SL, Ahmadsaidulu S, Swain R, Panigrahy AK (2022) Design and Modelling of Highly Sensitive Glucose Biosensor for Lab-on-chip Applications. Silicon, pp.1-7. https://doi.org/10.1007/s12633-021-01543-0

  33. Bajpai G, Gupta A, Prakash O, Pahwa G, Henkel J, Chauhan YS, Amrouch H (2020) ‘‘Impact of radiation on negative capacitance FinFET’’. Proc IEEE Int Rel Phys Symp (IRPS), 1–5

  34. Prakash O, Gupta A, Pahwa G, Henkel J, Chauhan YS, Amrouch H (2020) ‘Impact of interface traps on negative capacitance transistor: Device and circuit reliability.’ IEEE J Electron Devices Soc 8:1193–1201

Download references

Acknowledgements

The authors are thankful to Gokaraju Rangaraju Institute of Engineering & Technology, Hyderabad for their cooperation and support during this research work.

Author information

Authors and Affiliations

Authors

Contributions

R Deepa, M.Parimala Devi, N Arun Vignesh and S Kanithan: Conceptualization; R Deepa, M.Parimala Devi, and N Arun Vignesh: investigation; R Deepa, M.Parimala Devi, N Arun Vignesh and S Kanithan: resources; R Deepa, M.Parimala Devi, N Arun Vignesh and S Kanithan: data curation; R Deepa, M.Parimala Devi, N Arun Vignesh and S Kanithan: writing—original draft preparation; R Deepa, M.Parimala Devi, N Arun Vignesh and S Kanithan: writing—review and editing; R Deepa, M.Parimala Devi, N Arun Vignesh and S Kanithan: visualization; R Deepa: supervision.

Corresponding author

Correspondence to R. Deepa.

Ethics declarations

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects.

Consent to Participate

Yes.

Consent for Publication

Author(s): R Deepa.

Conflict of Interest

The authors declare that they have 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deepa, R., Devi, M.P., Vignesh, N.A. et al. Implementation and Performance Evaluation of Ferroelectric Negative Capacitance FET. Silicon 14, 2409–2419 (2022). https://doi.org/10.1007/s12633-022-01722-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12633-022-01722-7

Keywords

Navigation