Characterisation of the plasma density with two artificial neural network models

doi:10.1088/1674-1056/19/7/070505

Abstract This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is the target output neuron: the plasma density. The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe. The effectiveness of two artificial neural network models are demonstrated, the results show good agreements with corresponding experimental data. The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded, and the radial based function is more suitable than the multi layer perceptron in this work.

Keywords: plasma density prediction multi layer perceptron radial based function

Received: 25 April 2009
Revised: 04 August 2009
Accepted manuscript online:

PACS:	52.25.Jm	(Ionization of plasmas)
	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	52.70.Ds	(Electric and magnetic measurements)
	52.77.Dq	(Plasma-based ion implantation and deposition)
	52.50.Sw	(Plasma heating by microwaves; ECR, LH, collisional heating)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 60375012).

Cite this article:

Wang Teng (王腾), Gao Xiang-Dong (高向东), Li Wei (李炜) Characterisation of the plasma density with two artificial neural network models 2010 Chin. Phys. B 19 070505

[1]	Toader E I 2004 Plasma Sources Sci. Technol. bf 13 646
[2]	Chen J F and Ren Z X 1999 Vacuum 52 411
[3]	Mayuko K and Hiroshi M 2006 Vacuum 80 771
[4]	Jin X Y, Qiu X J and Zhu Z Y 2006 Acta Phys. Sin. 55 5338 (in Chinese)
[5]	Wang L, Cao J X, Wang Y, Niu T Y, Wang G and Zhu Y 2007 it Acta Phys. Sin. 56 1429 (in Chinese)
[6]	Hiroshi M, Doan H T and Yoshinobu K 2005 Surf. Coat. Technol. 200 850
[7]	Musil J 1996 Vacuum 47 145
[8]	Wang Y S, Sun J, Wang C J and Fan H D 2008 Acta Phys. Sin. 57 6120 (in Chinese)
[9]	Wang H, Wang A K, Yang Q W, Ding X T, Dong J Q, Sanuki H and Itoh K 2007 Chin. Phys. 16 3738
[10]	Du X L, Chen G C, Jiang D Y, Yao X Z and Zhu H S 1999 Acta Phys. Sin. 48 257 (in Chinese)
[11]	Dorteoust, Wu H M and Graves D B 1994 Plasma Sources Sci. Technol. 3 25
[12]	Lungu C P and Iwasak K 2002 Vacuum 66 197
[13]	Wu H M, Graves D B and Porteos R K 1995 Plasma Sources Sci. Technol. 4 22
[14]	Sterjovski Z and Nolan D 2006 J. Mater. Proc. Technol. 170 536
[15]	Xu L J and Xing J D 2007 Mater. Des. 28 1425
[16]	Bezerra E M and Ancelotti A C 2007 Mater. Sci. Eng. A 464 177
[17]	Scott D J and Coveney P V 2007 J. Eur. Cerma. Soc. bf 27 4425
[18]	Ming D J, Chyuan D L and Wang J T 2005 Appl. Surf. Sci. 245 290
[19]	Zhang G and Guessasma S 2006 Surf. Coat. Technol. bf 200 2610
[20]	Hakan C and Hasan O 2006 Wear 261 064
[21]	Pankaj S and Deo M C 2007 Applied Soft Computing 7 968

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