Advances in OptoElectronics

Volume 2015 (2015), Article ID 781327, 6 pages

http://dx.doi.org/10.1155/2015/781327

## Effective Evaluation of the Noise Factor of Microchannel Plate

^{1}School of Information and Electrical Engineering, Ludong University, Yantai 264025, China^{2}School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received 13 June 2015; Accepted 13 August 2015

Academic Editor: Jung Y. Huang

Copyright © 2015 Honggang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

To improve the noise performance of microchannel plate (MCP), we have presented a method using the sine random signals with Poisson distribution as the noise-excitation for electron source. By using this method, the effective evaluation of noise characteristics of MCP has been implemented through measuring and analyzing its noise factor. The results have demonstrated that the noise factor of filmed MCP is lower than 1.8. Additionally, as the open area ratio and the input electron energy are 72% and 400 eV, respectively, the noise characteristics of unfilmed MCP are improved evidently. Moreover, larger open area ratio, higher input electron energy, and higher voltage across the MCP all can reduce effectively the noise factor within a certain range. Meanwhile, the ion barrier film extends the life of image tube but at the cost of an increased noise factor. Therefore, it is necessary that a compromise between the optimum thickness of ion barrier film, open area ratio, input electron energy, and voltage across the MCP must be reached.

#### 1. Introduction

Modern photoelectric imaging intensifiers for various applications often employ a microchannel plate (MCP) to generate electronic gain by secondary electron multiplication [1]. It is interesting to investigate the noise performance of the MCP because our image intensifiers are affected by poor signal-to-noise ratio (SNR). By researching the noise characteristics of the critical component of the system, that is, MCP, it is possible to optimize the image intensifier design to the lowest noise levels possible. Since the MCP is used in imaging applications as an amplifier, the concept of a noise factor () is appropriate [2].

For the last decades, has been generally used as a criterion of evaluating the MCP noise characteristics, and further a substantial improvement is reduced [3–7]. Therefore, it is very important to effectively evaluate of MCP in image intensifiers. Currently, the noise characteristics of entire image intensifier, the effect of parameters of MCP on image intensifier performance, and the fabrication of MCP have mainly been concerned, whereas little attention has been paid to the noise characteristics of MCP itself [8–11]. In addition, the disadvantage of results presented in [7] is that the values of input SNR and output SNR are obtained by constant filament current, which are not consistent with the condition of MCP operating in image intensifier. Moreover, according to the requirements of measurement, the bottleneck is the significant difference between very low input current density (10^{−11}–10^{−10} A/cm^{2}) and high SNR, which leads to the fact that the input current noise is overwhelmed by the ambient interference noise, and then the measurement becomes difficult. To address this problem, we present a method using the sine random signals with Poisson distribution as the noise-excitation for electron source, through which the varying filament current satisfying the measurement requirements is generated. Accordingly, the results agree with the practical condition of MCP; in other words, the objective evaluation of the noise characteristics of MCP can be achieved by determining , which has been experimentally verified.

#### 2. Evaluation Method

##### 2.1. Noise Factor

From a practical standpoint, since the number of electrons injected into MCP is random, the noise caused by the fluctuation in these electrons is the input noise. Accordingly, the signal-to-noise ratio at the input end () of MCP is defined as the ratio of the input average signal to the root-mean-square (RMS) deviation from its mean value. Besides, the signal-to-noise ratio at the output end () is formed by the imperfection of MCP itself and the statistical nature of the gain process in MCP. Correspondingly, is expressed as the ratio of the output average signal to the root-mean-square (RMS) deviation from its mean value. It is worth noting that the noise characteristic of MCP is affected not only by that of MCP itself but also by , although this characteristic can be appraised through . Hence, of MCP is defined as [2]

From formula (1), it can be seen that the deterioration of SNR caused by MCP itself can be represented with due to eliminating the impact of , and thus the noise characteristic of MCP is analyzed objectively. Concretely, smaller stands for better performance of noise suppression.

##### 2.2. Implementation of the Electron Beam Incorporating Noise-Excitation

The electron source plays an important role in the measuring system, and it generates electron beam to be accelerated by an electric field to the MCP. In fact, the electrons are given off from a photocathode at random times, and their distribution for number of electrons generated per unit time interval is governed by Poisson distribution. To be more consistent with the actual work conditions of image intensifier, we use a group of sinusoidal signals with Poisson distribution as the noise-excitation, thereby generating the electron beam incorporating noise-excitation required by measurement. In the following, the generation of these signals is described.

To begin with, a random vector with Poisson distribution is generated, and it takes the form of where, here, stands for the transpose operation. Second, to meet the requirements of harmonic coefficient, a new even symmetry vector must be constructed on the basis of , and it is given by Furthermore, the elements of are expressed as where, here, represents the time index in time domain. And then, the sampling interval in frequency domain is set as , for obtaining the coefficients and frequencies of harmonic. Correspondingly, the coefficient of th harmonic is given by where denotes the normalized frequency index. From formula (5), we can obtain Finally, the sinusoidal signals obeying Poisson distribution as the noise-excitation can be obtained through the expression of Subsequently, the sine random signals with Poisson distribution are shown in Figure 1.