Adaptive Sampling Algorithm for ANN-based Performance Modeling of Nano-scale CMOS Inverter
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Adaptive Sampling Algorithm for ANN-based Performance Modeling of Nano-scale CMOS Inverter

Authors: Dipankar Dhabak, Soumya Pandit

Abstract:

This paper presents an adaptive technique for generation of data required for construction of artificial neural network-based performance model of nano-scale CMOS inverter circuit. The training data are generated from the samples through SPICE simulation. The proposed algorithm has been compared to standard progressive sampling algorithms like arithmetic sampling and geometric sampling. The advantages of the present approach over the others have been demonstrated. The ANN predicted results have been compared with actual SPICE results. A very good accuracy has been obtained.

Keywords: CMOS Inverter, Nano-scale, Adaptive Sampling, ArtificialNeural Network

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1073529

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[1] Georges.G.E. Gielen and Rob.A. Rutenbar. Computer-Aided Design of Analog and Mixed-Signal Integrated Circuits. Proceedings of the IEEE, Vol.88:pp.1825-1852, December 2000.
[2] T. McConaghy and G. Gielen. Automation in Mixed-Signal Design: Challenges and Solutions in the Wake of the Nano Era. In Proc. of ICCAD, pages 461-463, November 2006.
[3] Rob.A. Rutenbar, Georges.G.E. Gielen, and J.Roychowdhury. Hierarchical Modeling, Optimization, and Synthesis for System-Level Analog and RF Designs. Proceedings of the IEEE, Vol.95:pp.640-669, March 2007.
[4] W. Daems, G. Gielen, and W. Sansen. Simulation-Based Generation of Posynomial Performance Models for the Sizing of Analog Integrated Circuits. IEEE Trans. CADICS, Vol.22:pp.517-534, May 2003.
[5] M. Avci and T. Yildirim. Neural Network Based MOS Transistor Geometry Decision for TSMC 0.18 Process Technology. In Proc. of ICCS, pages 615-622, 2006.
[6] F. Gunes, F. Gurgen, and G. Torpi, H. Signal-noise neural network model for active microwave devices. IEE Proceedings Circuits Devices and Systems, Vol.143:pp.1-8, 1996.
[7] N. Kahraman and T. Yildirim. Technology Independent Circuit Sizing for Fundamental Analog Circuits using Artificial Neural Networks. In Proc. of PRIME, pages 1-4, 2008.
[8] N. Kahraman and T. Yildirim. Technology independent circuit sizing for standard cell based design using neural network. Digital Signal Processing, Vol.19:pp.708-714, 2009.
[9] F. Djeffala, M. Chahdib, A. Benhayaa, and M.L. Hafianea. An approach based on neural computation to simulate the nanoscale CMOS circuit. Solid State Electronics, Vol.51:pp.48-56, 2007.
[10] S.K. Mandal, S. Sural, and A. Patra. ANN-and PSO-Based Synthesis of On-Chip Spiral Inductors for RF ICs. IEEE Transaction CADICS, Vol.27:pp.188-192, January 2008.
[11] G.H. John and P. Langley. Static Versus Dynamic Sampling for Data Mining. In Proc. of Knowledge Discovery and Data Mining, 1996.
[12] F. Provost, D. Jensen, and T. Oates. Efficient Progressive Sampling. In Proc. of Knowledge Discovery and Data Mining, pages 23-32, 1999.
[13] C. Meek, B. Thiesson, and D. Heckerman. The Learning-Curve Sampling Method Applied to Model-Based Clustering. Journal of Machine Learning Research, Vol.2:pp.397-418, February 2002.
[14] A. Satyanarayana and I. Davidson. A Dynamic Adaptive Sampling Algorithm for Real World Applications: Finger Print Recognition and Face Recognition. In Proc. of ISMIS, pages 631-640, 2005.
[15] V.K. Devabhaktuni and Q.J. Zhang. Neural Network Training-Driven Adaptive Sampling Algorithm for Microwave Modeling. In Proc. of European Microwave Conference, 2000.
[16] G. Wolfe and R. Vemuri. Adaptive Sampling and Modeling of Analog Circuit Performance Parameters with Pseudo-Cubic Splines. In Proc. of ICCAD, pages 931-836, 2004.
[17] D. Dhabak and S. Pandit. Performance Modeling of Nano-scale CMOS Inverter using Artificial Neural Network. In Proc. of IESPC, pages 33- 36, 2011.
[18] Q.J. Zhang, K.C. Gupta, and V.K. Devabhaktuni. Artificial Neural Networks for RF and Microwave Design: From Theory to Practice. IEEE Trans. MTT, Vol.51:1339-1350, April 2003.
[19] W. Zhao and Y. Cao. New Generation of Predictive Technology Model for Sub-45 nm Early Design Exploration. IEEE Transactions Electron Devices, Vol.53:pp.2816-2823, November 2006.
[20] L. Kuipers and H. Niederreiter. Uniform distribution of sequences. Dover Publications.
[21] I. C. Yeh. Modeling of Strength of High-Performance Concrete using Artificial Neural Network. Cement and Concrete Research, Vol.28:pp.1797-1808, 1998.
[22] http://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength.