iosr-JEEE   Volume-8 ~ Issue-1

Abstract: In the world of Integrated Circuits, Complementary Metal–Oxide–Semiconductor (CMOS) has lost its efficiency during scaling beyond 32nm. Scaling causes severe Short Channel Effects (SCE) which are difficult to hold back. As a result of such SCE many alternate devices have been studied. Some of the major contestants include Multi Gate Field Effect Transistor (MuGFET) like FinFET, Nano tubes, Nano wires etc. In this work, the basic gates and memory circuits like DRAM are designed in HSPICE software using CMOS structure and FinFET structures are analyzed and their performances like average power, standby power dissipation and leakage power are compared. A 32nm technology FinFET model is used to design DRAM.

Keyword: CMOS, Dynamic RAM, FinFET, Memory Cell, and Power Dissipation.

[1] Kidong Kim, Ohseob Kwon, Jihyun Seo and Taeyoung Won "Nanoscale Device Modelling and Simulation: Fin Field Effect Transistor ,‟ Japan Journal of Applied Physics. 43 (2004) pp. 3784-3789.
[2] Brian Doyle, Reza Arghavani, Doug Barlage, Suman Datta, Mark Doczy, Jack Kavalieros, Anand Murthy and Robert Chau, "Transistor Elements for 30nm Physical Gate Lengths and Beyond", Intel Technology Journal , volume 6 , Issue 2, May 2002.
[3] D.Hisamoto, W.C.Lee, J.Kedzierski , H.Takeuchi , C.Kuo ,E.Anderson, T.J.King , J.Bokor and C.Hu "FinFET- A self Alligned Double Gate MOSFET scalable to 20 nm", IEEE Trans. Electron Devices, Vol.47, No. 12,pp 2320-2325, 2000.
[4] Kunihoro Suzuki, Tetsu Tanaka ,Yoshiharu Tosaka , Hiroshi Horie and Yoshihiro Arimoto, "Scaling Theory
for Double-Gate SO1 MOSFET‟s", IEEE Trans. Electron Devices, Vol.40, No. 12,pp 2326-2329, 1993.
[5] A. Carlson, Z. Guo, S. Balasubramanian, L.-T. Pang, T.-J. King Liu, and B. Nikolic "FinFET SRAM with Enhanced Read / Write Margins", 2006 IEEE International SOI Conference Proceedings, PP 121-126.
[6] Chevillon, N. Mingchun Tang Pregaldiny, F. Lallement, C. and Madec, M. "FinFET compact modeling and parameter extraction", Mixed Design of Integrated Circuits & Systems, 2009. MIXDES '09. MIXDES-16th International Conference ,2009.

[7] C. Auth,P. Bai, et al(2005), "A 65nm logic technology featuring 35nm gate lengths, enhanced channel strain, 8 Cu interconnect layers, low-k ILD and 0.57 nm SRAM cell,‟ presented at 2004 International Electron Devices Meeting. San Francisco, CA.

Abstract: Power quality improvement has become a major area of concern in present era. Due to the increase in modern sensitive and sophisticated loads connected to the Distribution System it has been very important to improve the quality of power because nonstandard voltage, current or frequency results a failure of the loads connected to the systems. Power electronics and advanced control technologies have made it possible to improve the quality of power and operate the sensitive loads satisfactorily. One of the major problems dealt in this paper is the voltage quality which is very severe for the industrial customers as it can cause malfunctioning of several sensitive electronic equipments. Dynamic Voltage Restorer (DVR) is a solution to improve voltage quality, which is connected in series with the network. This paper presents modelling, analysis and simulation of DVR in MATLAB SIMULINK, which includes PI controller and discrete PWM generator for control purpose of DVR. Simulation results of performance of DVR under different fault conditions such as single line to ground fault (SLG), double line to ground fault (DLG), line to line fault (L-L), three phase to ground fault etc. are presented in this paper. The results showed clearly the performance of the DVR in voltage quality improvement. Keywords: Dynamic Voltage Restorer (DVR), voltage quality, PI controller, Pulse Width Modulation (PWM), power quality.

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[3]. N.G. Hingorani, "Introducing Custom Power", IEEE Spectrum, vol. 32, pp. 41- 48, 1995 Distribution Custom Power Task Force, 2003.
[4]. R. H. Salimin, M.S. A. Rahim, "Simulation Analysis of DVR performance for voltage sag mitigation", the 5th international power Engineering and Optimization Conference (PEOCO2011), 2011
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[7]. M. H. Haque, "Compensation of Distribution System Voltage Sag by DVR and DSTATCOM", IEEE Porto Power Tech Conference, vol. 1, 2002.
[8]. Yash Pal, A. Swarup, Senior Member, IEEE, and Bhim Singh, Senior Member, IEEE "A Review of Compensating Type Custom Power Devices for Power Quality Improvement" IEEE Power India Conference, pp. 1-8, 2008.
[9]. Bingsen Wang, Giri Venkataramanan and Mahesh Illindala, "Operation and Control of a Dynamic Voltage Restorer Using. Transformer Coupled H-Bridge Converters", IEEE transactions on Power electronics, vol. 21, pp. 1053-1061, July06
[10]. Rosli Omar, N.A. Rahim and Marizan Slaiman, "Dynamic Voltage restorer Application for Power Quality improvement in Electrical Distribution System" Australian Journal of Basic and applied Sciences, pp 379-396, 2011

Abstract: This paper presents the Matlab Simulation of fuzzy logic based Sensorless indirect vector control of induction motor with a rotor resistance adaptation. Here the fuzzy controller used for speed controller offers superior transient performance when compared with the conventional control algorithms using PI controller. Rotor resistance of the motor will change significantly with temperature and frequency. This variation has a major influence on the field oriented control performance of an induction motor due to the deviation of slip frequency from the set value. This paper uses neural learning algorithm based rotor resistance adaptation scheme for making the system robust against rotor resistance variation.

Keywords: Fuzzy Logic Controller, Indirect Field Orientation control, MRAS Approach, Neural Learning Algorithm, Rotor Resistance estimation, Sensorless Control.

[1] Bimal K Bose,‟ Modern Power Electronics and AC Drives‟, Third edition, INDIA: Pearson Education, Inc., 2007.
[2] Birachi Narayan Kar, K B Mohanty, "Indirect Vector Control of Induction Motor Using Fuzzy logic Controller‟, IEEE 2011.
[3] Iulian Birou , Virgril Maier, Sorin Pavel, Calin Rusu, ―Indirect Vector Control of an Induction Motor with Fuzzy Logic based Speed Controller‟; Advances in Electrical and Computer Engineering, Volume 10 Number 1, 2010.
[4] B. K. Bose, ―Neural network applications in power electronics and motor drives—An introduction and perspectives,‖ IEEE Trans. Ind.Electron., vol. 54, no. 1, pp. 14–33, Jan. 2007.
[5] C. Schauder, ―Adaptive speed identification for vector control of in-duction motors without rotational transducers,‖ IEEE Trans. Ind. Appl.,vol. 28, no. 5, pp. 1054–1061, Sep./Oct. 1992..
[6] Baburaj Karanayil, Member, IEEE, Muhammed Fazlur Rahman, Senior Member, IEEE, and Colin Grantham-― Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive‖ IEEE transactions on industrial electronics, vol. 54, no. 1, February 2007.