Abstract
Full adder is one of the important components in electronics, used for various fundamental processing algorithms such as addition and multiplication. The application of these full adders is included in but not limited to Very Large-Scale Integration (VLSI) and Digital Signal Processing (DSP). To provide scalability and reliability to the advanced algorithms for high-end applications, the designing system of full adder should be enhanced. So, in this paper, we intended to improve the efficiency of a full adder circuit to work under low power and delay conditions. The software we used in this project is MENTOR GRAPHICS using 180 nm technology. The efficiency of the proposed transistor design is evaluated by analysing the power consumption, delay, PDP, capacitor load, delay w.r.t capacitance and PDP w.r.t capacitance. The parameters are compared between our proposed design and the literature schemes such as OLPFAD, DFEFA, DTLPCFA, and DPEHFA, respectively. It is evident that our proposed design outperforms the other.
Similar content being viewed by others
Data Availability
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
Code availability
The code that is used this study are available from the corresponding author, upon reasonable request.
References
Mandar, G., Rajiv, C., Nikhil, M., Siddhesh, J., Sangeeta, J. (2017). Design of 16-transistor Full Adder Circuit Using 6-transistor XNOR Gates. IEEE.
Navi, K., Maeen, M., Foroutan, V., Timarchi, S., & Kavehei, O. (2009). A novel low-power full-adder cell for low voltage. Integration, 42(4), 457–467.
Wang, D., Yang, M., Cheng, W., Guan, X., Zhu, Z., & Yang, Y. (2009). Novel low power full adder cells in 180nm CMOS technology. In: 2009 4th IEEE Conference on Industrial Electronics and Applications (pp. 430–433). IEEE.
Shams, A. M., Darwish, T. K., & Bayoumi, M. A. (2002). Performance analysis of low-power 1-bit CMOS full adder cells. IEEE transactions on very large scale integration (VLSI) systems, 10(1), 20–29.
Shipra, M., Shelendra Singh, T., Shyam A., (2013). Design low power 1OT full adder using process and circuit techniques. In: IEEE Proceedings of 7th international conference on intelligent systems and control (ISCO 2013). IEEE.
Sudhanshu, J., Shaurakar, D., (2018). Design and performance evaluation of hybrid full adder for extensive PDP reduction. In: IEEE 2018 3rd international conference for convergence in technology (I2CT).
Goel, S., Kumar, A., & Bayoumi, M. A. (2006). Design of robust, energy-efficient full adders for deep-submicrometer design using Hybrid-CMOS logic style. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, 14(12), 1309–1321.
Moradi, F., Wisland, D. T., Mahmoodi, H., Aunet, S., Cao, T. V., & Peiravi, A. (2009). Ultra low power full adder topologies. In: 2009 IEEE International Symposium on Circuits and Systems (pp. 3158–3161). IEEE.
Vijaya, L. A., Nagendra Babu, K. V. T., Sree Ram Deepak, M., Sai, K. A., Chandra Sekhar Yadav, G. V. P., Gopi, T. V., & Ghali, V. S. (2019). A machine learning based approach for defect detection and characterization in non-linear frequency modulated thermal wave imaging. International Journal of Emerging Trends in Engineering Research, 7(11), 517–522.
Murthy, A. S., Murthy, P. S., Rajesh, V., Ahammad, S. H., & Jagan, B. O. (2019). Execution of natural random forest machine learning techniques on multi spectral image compression. International Journal of Pharmaceutical Research, 11(4), 1241–1255.
Sreedhar, B. S., & Bojja, P. (2019). Machine learning algorithms for MR brian image classification. International Journal of Recent Technology and Engineering, 8(3), 6744–6747.
Maddisetti, L., Senapati, R. K., & Ravindra, J. V. R. (2019). Supervised machine learning for training a neural network as 5:2 compressor. International Journal of Innovative Technology and Exploring Engineering, 8(10), 2079–208.
Dudi, B., & Rajesh, V. (2019). Medicinal plant recognition based on CNN and machine learning. International Journal of Advanced Trends in Computer Science and Engineering, 8(4), 999–1003.
Maddisetti, L., Senapati, R. K., & Ravindra, J. V. R. (2019). Training neural network as approximate 4:2 compressor applying machine learning algorithms for accuracy comparison. International Journal of Advanced Trends in Computer Science and Engineering, 8(2), 211–215.
Raju, K., Pilli, S. K., Kumar, G. S. S., Saikumar, K., & Jagan, B. O. L. (2019). Implementation of natural random forest machine learning methods on multi spectral image compression. Journal of Critical Reviews, 6(5), 265–273.
Mittal S., Mittal V.K. (2019) Biomedical requirements for human machine interface towards building a humanoid: A review. In: 2019 IEEE 16th India council international conference, INDICON 2019 - Symposium Proceedings, pp. 1–6.
Sripath, R. K., Roopkanth, K., Uday, T. V., Bhavana, V., & Priyanka, J. (2018). Student career prediction using advanced machine learning techniques. International Journal of Engineering and Technology (UAE), 7(2), 26–29.
Danthala, S., Rao, S., Mannepalli, K., & Shilpa, D. (2018). Robotic manipulator control by using machine learning algorithms: A review. International Journal of Mechanical and Production Engineering Research and Development, 8(5), 305–310.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Author information
Authors and Affiliations
Contributions
Conceptualization: K. Praghash; Methodology: S Arunmetha; Formal analysis and investigation: K. Praghash & S. Arunmetha; Writing-original draft preparation: S. Arunmetha & K. Praghash, Writing-review and editing: B. Sai Tanuja, K. Preethi, N. P. N. S. Chandana.
Corresponding author
Ethics declarations
Conflict of interests
The author(s) have no conflicts of interest to declare.
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Praghash, K., Arun Metha, S., Sai Tanuja, B. et al. Implementation and Investigation of an Optimal Full Adder Design for Low Power and Reduced Delay Conditions. Wireless Pers Commun 126, 3041–3069 (2022). https://doi.org/10.1007/s11277-022-09851-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-022-09851-3