OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 22, Iss. 2 — Jan. 27, 2014
  • pp: 1302–1312
« Show journal navigation

Measurement of surface parameters from autocorrelation function of speckles in deep Fresnel region with microscopic imaging system

Chunxiang Liu, Qingrui Dong, Haixia Li, Zhenhua Li, Xing Li, and Chuanfu Cheng  »View Author Affiliations


Optics Express, Vol. 22, Issue 2, pp. 1302-1312 (2014)
http://dx.doi.org/10.1364/OE.22.001302


View Full Text Article

Acrobat PDF (1301 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

The derived two-dimensional autocorrelation function of speckles in the deep Fresnel region shows that it is related to the scattering of rough surface with the scattered intensity profile acting as the aperture function. We propose the method that is convenient for measuring surface parameters from the normalized autocorrelation function of speckles acquired with a microscopic imaging system. In experiment, a multi-scale behavior of the speckles has been identified, which is compatible with fractal character. With the speckle intensity data, we calculate the normalized autocorrelation function of the speckles and extract the roughness, the lateral correlation length and the roughness exponent of the random surface samples by fitting the expression to the autocorrelation function data. Comparison of the results with an atomic force microscopic measurements shows that our method has a satisfying accuracy.

© 2014 Optical Society of America

1. Introduction

2. Theory

In the typical light scattering diagram shown in Fig. 1(a), a parallel laser beam with wavelength λ illuminates a rough scattering surface.
Fig. 1 (a) Schematic diagram for generating and observing the speckle fields in deep Fresnel region. (b) Diagram of the equivalent aperture contributing to the intensity of the point x = 0.
The observation plane at distance z from the random surface is in the deep Fresnel region, which refers to the diffraction region from a few wavelengths near the surface to the traditional Fresnel region. We suppose that zis small and the diameter D of the illuminating beam is large, so that the scattered waves will spread and then enough scattering elements on the screen may contribute to the light field at an observation point. Figure 1(b) gives the schematic diagram of the superposition of the light waves scattered from an equivalent scattering aperture contributing to the intensity of a point on the observation plane, which represents the one-dimensional case shown in Fig. 1(a) with the red dashed line. From the scattering theory, we know that the light intensity scattered onto the observation plane from a small adjacent region of a point x0on the random surface is distributed in the form of a decaying profile, which are shown in Fig. 1(b) with five small bell-shaped curves whose centers are at the conjugate positions a, b, c, d, e, respectively. The profile broadens with the increase of the distance z. Correspondingly, the light intensity at an arbitrary point xon the observation plane is the superposition of the light waves scattered from an equivalent aperture on the random surface with its transmittance variation mirroring the decaying profile and its center at the conjugate point x0=x. The big bell-shaped curve in Fig. 1(b) represents the equivalent scattering aperture with the center at x0=0on the object plane and hence is that contributing to the intensity of point x=0on the observation plane.

By use of Kirchhoff approximation and with the terms of surface height derivatives neglected, the scattered light field on the observation plane(x,y)can be written as [14

14. J. A. Ogilvy, Theory of Wave Scattering from Rough Surfaces (Adam Hilger, 1991).

]:
U(x,y)=exp[ik(n1)h(x0,y0)]exp(ikr)rcosθdx0dy0,
(1)
where h(x0,y0)is the height function of the surface, n is the refractive index of the screen, cosθ is the inclination factor, r=[z2+(xx0)2+(yy0)2]1/2is the distance from the object point to the observation point, and k=2π/λ is the magnitude of the scattered light wavek. I(x,y)=<U(x,y)U*(x,y)>is the scattered intensity at point(x,y)and the autocorrelation function of the scattered intensity is given by:
RI(x1,y1;x2,y2)=<I(x1,y1)I(x2,y2)>,
(2)
whererepresents the ensemble average. The normalized autocorrelation function of the intensity fluctuation is defined by [15

15. J. C. Dainty, The Statistics of Speckle Patterns, in Progress in Optics Vol. XIV, (North-Holland, 1976).

]:
γI(x1,y1;x2,y2)=[RI(x1,y1;x2,y2)<I>2]/<I>2,
(3)
where <I>=<I(x1,y1)>=<I(x2,y2)> is the average intensity of the speckle ðelds. γI(x1,y1;x2,y2) may also be called autocovariance, and it tends to zero when the two points are moved away from each other. Compared with the autocorrelation function RI(x1,y1;x2,y2), the width of γI(x1,y1;x2,y2) can be well-defined. When U(x,y) is the circular complex Gaussian ðeld, the autocorrelation function of the speckle intensity takes the form
RI(x1,y1;x2,y2)=<I>2+|<U(x1,y1)U(x2,y2)>|2,
(4)
Thus, the correlation function γI(x1,y1;x2,y2) assumes form:
γI(x1,y1;x2,y2)=|<U(x1,y1)U(x2,y2)>|2/<I>2,
(5)
The problem of calculating γI(x1,y1;x2,y2) is thus reduced to that of calculating the autocorrelation of the fields.
RU(x1,y1;x2,y2)=<U(x1,y1)U(x2,y2)>,
(6)
Subsitituting Eq. (1) to Eq. (6) and using the following statistical principle about the average calculation [16

16. J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications (Ben Roberts & Company, 2007).

]:
<exp{ik(n1)[h(x01,y01)-h(x02,y02)]}>=pj[h(x01,y01),h(x02,y02)]exp{ik(n1)[h(x01,y01)-h(x02,y02)]}dx01dy01dx02dy02,
(7)
where pj[h(x01,y01),h(x02,y02)] is the joint-probability density function of surface height functions. After some calculations, we derive the following expression:
<exp{ik(n1)[h(x01,y01)-h(x02,y02)]}>=exp{-[k(n1)]2[w2-Rh(x01,y01;x02,y02)]},
(8)
where Rh(x01,y01;x02,y02) is the correlation function of surface height. Combinning with Eqs. (6)-(8) and continuing calculations, we get:
RU(x1,y1;x2,y2)=RU(Δx,Δy)=λz2exp{-[k(n1)]2[w2-Rh(Δx0,Δy0)]}×exp[i2πkpx0x)]exp[i2πkqy0y)]dΔx0dΔy0dkpdk,
(9)
WhereΔx=x2x1,Δy=y2y1,Δx0=x02x01,Δy0=y02y01 and kp=sinθcosγ/λ,kq=sinθsinγ/λ are the components of wave vector kon the object plane, respectively, θ and γ are the azimuths angles of k. The integration of Δx0andΔy0 in Eq. (9) is the duplex Fourier transform of the functionF(Δx0,Δy0)=exp{-[k(n1)]2[w2-Rh(Δx0,Δy0)]}, furthermore, FT1[F(Δx0,Δy0)] is actually the scattered intensity profile:
I(k||)=exp{[k(n1)]2[w2Rh(ρ)]}exp(ik||ρ)d2ρ=FT1[F(Δx0,Δy0)],
(10)
where k||=2π(kpi+kqj) is the parallel vector of k,iandjare the unit vectors, andρ= |ρ|=x0)2+y0)2 is the distance between the two points on the object plane.

Obviously, in the deep Fresnel region the scattered intensity profile takes the place of the scattering aperture in the Van Cittert-Zernike theorem of the far-field form. Afterward, substituting Eqs. (9) and (10) into Eq. (5), we finally obtain
γI(Δx,Δy)=exp{-2[2π(n1)/λ]2[w2-Rh(Δx,Δy)]},
(11)
The above equation reveals the quantitative relation of the normalized autocorrelation function γI(Δx,Δy) and the autocorrelation function of the random surface Rh(Δx,Δy). It has been demonstrated that random self-affine fractal surface model [17

17. Y. P. Zhao, G. C. Wang, and T. H. Lu, Characterization of Amorphous and Crystalline Rough Surfaces: Principles and Applications (Academic, 2001).

] can be well used to describe the ground glass screens [18

18. D. P. Qi, D. L. Liu, S. Y. Teng, N. Y. Zhang, and C. F. Cheng, “Morphology analysis atomic force microscope and light scattering study for random scattering screens,” Acta Phys. Sin. 49(7), 1260–1266 (2000).

] and its height autocorrelation function is given by [19

19. Y. P. Zhao, I. Wu, C. F. Cheng, U. Block, G. C. Wang, and T. M. Lu, “Characterization of random rough surfaces by in-plane light scattering,” J. Appl. Phys. 84(5), 2571–2582 (1998). [CrossRef]

]:
Rh(ρ)=<h(r0)h(r0+ρ)>=w2exp[(ρ/ξ)2α],
(12)
where wis root-mean-square deviation roughness, ξ is the lateral correlation length, and αis roughness exponent of the surface.

In order to deeply understand the influence of the above surface parameters on the speckle patterns, we will firstly analyse their effects on the scattered profiles which determine the distribution of correlation function γI(Δx,Δy). We have simulated the scattered profile produced by random surfaces with different parameters. In the simulation, we generate 2000 one-dimensional self-affine surface samples and take them as one surface ensemble [20

20. C. F. Cheng, C. X. Liu, S. Y. Teng, N. Y. Zhang, and M. Liu, “Half-width of intensity profiles of light scattered from self-affine fractal random surfaces and simulational verifications,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 65(6), 061104 (2002). [CrossRef] [PubMed]

]. Based on Eq. (10), we calculate the corresponding light field U(k||) and the ensemble average intensity I(k||)=<Iu(k||)>=<U(k||)U(k||)>. Figures 2(a)-2(c) give the simulated profiles scattered by surface samples with different parameters.
Fig. 2 The scattered intensity profiles produced by random surfaces with different parameters.
Through careful observation, we find that the distributions of the scattered profiles with the change of surface parameters are obviously different. The increase of the roughness w, the decrease of the lateral correlation length ξ or the roughness exponent α will broaden the width of scattered profiles. The average intensity I(k||) drops slowly with the increase of k|| as shown in Figs. 2(a) or 2(b) but drops rapidly shown in Fig. 2(c). It may be difficult to see clearly the difference of the shape of the curves in Figs. 2(a)-2(c) by naked eyes, but more quantitative description can be fulfilled by the full-width at half maximum, whose approximate expression is written as Wp=2ξ1{lnΩln[exp(Ω-1)+1-1/e]}-1/2α with Ω=[2π(n-1)/λ]2w2 for transmissive random surface samples [20

20. C. F. Cheng, C. X. Liu, S. Y. Teng, N. Y. Zhang, and M. Liu, “Half-width of intensity profiles of light scattered from self-affine fractal random surfaces and simulational verifications,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 65(6), 061104 (2002). [CrossRef] [PubMed]

]. This expression may depict to some extent the sensitivity of the surface parameters influencing the scattered profiles. Additionally, for the broadest curves in Figs. 2(a) and 2(b) drop so slowly that truncations seemingly exist. Such truncations may really happen and can be overcome by using an objective of high numerical aperture to collect large scattering-angle waves. Overall, the differences of the scattered profiles as an equivalent scattering aperture will lead to the change of the distribution of correlation function γI(Δx,Δy). Therefore, we can extract the surface parameters through these changes of γI(Δx,Δy), i.e., we can firstly calculate γI(Δx,Δy)with the data of the measured speckle intensities, and then extract the parameters of the random surface by fitting the curve of γI(Δx,Δy) using Eqs. (11) and (12).

3. Experimental study and discussion

3.1 Setup of the optical system and measurement of random surface samples with AFM

Figure 3 schematically shows the experimental system for acquiring the speckle field in deep Fresnel region.
Fig. 3 Diagram of the optical setup for collecting the speckle fields in deep Fresnel region.
We use a microscope objective (MO) with high numerical aperture (Nikon, Dry, 100x, N.A.0.9, WD 1mm) to collect a wider range of the spatial spectrum components scattered from the random surface. As is well known, the higher spatial spectrum components corresponds to the resolution for local tiny structure in the surface morphology, therefore, using this microscope objective to collect the speckle intensity will lead to the increase in the measurement resolution of surface characteristics. In the experiment, a laser beam with a wavelength of 532 nm is filtered and expanded with a spatial filter, and is paralleled to be a laser beam again by a collimating lens 1, and then it illuminates the random surface sample. The diameter D of the incident beam is 6cm and the distance z from the microscope objective to the random surface is about 1cm,so that the aperture contributing to an observation point is within the area of the illumination beam. For accurate and large-scale positioning, the sample of random surface is placed on a three-dimensional piezo nanometer stage (PI E516) which has been installed on a two-dimensional precise mobile platform. Lens 2 is used for convenient adjustment of the image magnification. The image is received by a CCD (Roper, Cascade 1k) with 16bit dynamic range and an array of 1004 × 1002 pixels (pixel size 8μm × 8μm). Since the system has a very small object distance (about work distance of MO 1mm) and a comparatively large image distance (several hundred mm), it is difficult for the position of the image plane to be exactly determined in the case of laser illumination. In the optical adjustment, we firstly use a white light source with optical fiber output to illuminate the sample, and move L2 and the CCD in the direction of optical axis for the rough determination of the image plane and the magnification. We use the piezo stage to adjust the object distance repeatedly to obtain the clearest image, and then move the mobile platform until the distance between microscope objective and the image plane is 1cm. Then we remove the white light source and use laser beam to illuminate the sample for acquiring the speckle pattern. Due to the high ability of collecting scattered light and the good resolution of the MO, the light intensity distributions in observation planes in the deep Fresnel region can be recorded. The imaging range of CCD for collecting the speckle field is 34 × 34μm2. We need not a beam stop in the setup to dispose the non-diffused beam as in Ref [8

8. M. Giglio, M. Carpineti, and A. Vailati, “Space intensity correlations in the near field of the scattered light: a direct measurement of the density correlation function g(r),” Phys. Rev. Lett. 85(7), 1416–1419 (2000). [CrossRef] [PubMed]

] for it disappears since the roughness values of the samples are large enough.

In the derivation of Eq. (5), the speckle fields in the region near the scattered screen are assumed to be circular Gaussian. For this to be satisfied, the roughness values of the surface samples need to be large enough so that the non-diffused component in the scattered waves, which results in partially developed speckles deviating from Gaussianity, should be negligible compared with the diffused component. In the experiment, we select holographic plate as glass substrate due to its higher flatness and grind three glass substrates with silicon carbide powders with sizes of 10µm, 14µm, and 20µm, respectively. Correspondingly, the three samples are labelled as sample No.1, No.2 and No.3. All samples are measured with an atomic force microscope (AFM, PARK, Autoprobe CP, Contact mode, UL20 tip). The scanned area is 80μm × 80μm and the data points are 256 × 256. For each sample, five AFM images are scanned in different parts of the sample and one of them is shown in Figs. 4(a)-4(c).
Fig. 4 AFM images of three random surfaces labeled as No.1, No. 2 and No. 3, respectively.
From these images, the height data h(x0,y0) as having appeared in Eq. (1) in the scanned area of each sample can be obtained. It is seen that sample surfaces are distributed with larger valleys and mounds, which constitutes the so-called scattering grains of the rough surface. On these valleys and mounds, there are smaller grains and fluctuations with different size scales. The root-mean-square roughness and numerical height-height correlation are obtained by averaging those quantities calculated from the data of each image. The average roughness values of the samples No.1, No. 2 and No.3 calculated from AFM image are w1=0.409±0.0013μm,w2=0.458±0.0012μm and w3=0.563±0.1559μm, respectively. These roughness values are large enough for the non-diffused component in the scattered waves to be neglected. Our experience is that for the samples we have fabricated in the above manner, the non-diffused component disappears when the roughness value is greater than about 0.3μm. We calculate the numerical distributions of the autocorrelation of the three samples and fit it with Eq. (12), respectively. Thus, the lateral correlation length and the roughness exponent of three samples can be obtained by fitting. The results are ξ1=4.147±0.212μm and α1=0.789±0.017 for No.1; ξ2=4.855±0.1829μm and α2=0.8056±0.0125for No.2; ξ3=5.859±0.156μm and α3=0.8225±0.0112 for No.3, respectively.

3.2 Measurement of surface parameters with the speckle method

With the optical system we constructed above, the images of speckle intensity distributions in the deep Fresnel region are obtained and shown in Figs. 5(a)-5(c) for surface samples No.1, No.2 and No.3, respectively.
Fig. 5 Speckle patterns for the samples No.1, No.2 and No.3, respectively.
Here we notice that the structures of the speckle patterns are different from the traditional far field speckles. In each of the patterns speckle grains have no obvious specific size and they appear the distribution in size of certain range from small to large scales. Moreover, the large-sized speckles appear intensity fluctuations consisted of many small-sized speckle grains and this differs from the smooth local intensity in the traditional far field speckles. These features indicate that the structures of speckle patterns in the deep Fresnel region have the characteristics of fractals and are distributed in multi-scaled sizes, and they are the reflection of the scattering properties of the surface topography.

Through careful observation of the speckle patterns in Figs. 5(a)-5(c), we may see that the speckle pattern in Fig. 5(a) has more small-sized grains and less large-sized grains, while in Fig. 5(c) there appears some very large grains as shown in the red circle except for many small-sized grains with the same scales as those in Fig. 5(a), This indicates that the average size of the speckles in Fig. 5(c) should be larger than that in Fig. 5(a). The speckle grain distribution in Fig. 5(b) is in-between Figs. 5(a) and 5(c). These qualitative characteristics may be quantitatively described by the autocorrelation function of the speckle intensity. As analysed in Section 2, the correlation function γI(Δx,Δy) is exactly determined by the scattered profile, and the detailed features of speckle grains are jointly determined by the factors such as the width of scattered profiles and the descendant shape of I(k||) with the increase of k||. Larger roughness w, smaller lateral correlation length ξ and smaller roughness exponent α will broaden the scattered profile, and then decrease the width of autocorrelation function γI(Δx,Δy), i.e., speckle grains. More definitely, the structures of the speckle grain described by γI(Δx,Δy) are the reflection of the surface topography.

According to the relationship between the height autocorrelation function and the height-height correlation functions of the random surface [14

14. J. A. Ogilvy, Theory of Wave Scattering from Rough Surfaces (Adam Hilger, 1991).

,17

17. Y. P. Zhao, G. C. Wang, and T. H. Lu, Characterization of Amorphous and Crystalline Rough Surfaces: Principles and Applications (Academic, 2001).

], the normalized intensity -intensity correlation functions of speckle intensity may also take the following form:
HIxy)=2σI2[1γIxy)],
(13)
whereσI2=<[I(x,y)<I>]2>is the root mean square speckle intensity fluctuation. Substituting Eq. (11) into the above equation and considering the case of ρ0, we finally obtain
HIxy)=2σI2{1exp{-2[2π(n-1)/λ]2[w2-Rhx,Δy)]}}=4σI2[2π(n-1)/λ]2w2(ρ/ξ)2α,
(14)
it can be seen that the approximation HI(ρ)ρ2α holds in the range of ρ0. From the speckle intensities measured with CCD shown in Figs. 5(a)-5(c), the autocorrelation function RIx,Δy)and the average intensity <I> are numerically calculated, and then γI(Δx,Δy) and HI(Δx,Δy) can be calculated based on Eqs. (3) and (13). Afterward, the linear fitting in the log-log scale will give the fractal exponent α of the surface samples. The results are as following α1=0.7832±0.02745for No.1; α2=0.7987±0.02228 for No.2 and α3=0.802±0.0117 for No.3. With the values of α, we obtain the other two surface parameters w and ξ by fitting the curve of γI(ρ)vs.ρ with Eqs. (11) and (12). Figures 6(a)-6(c) show the data of normalized autocorrelation functions calculated from the speckle intensities and the fit curves in solid lines for samples No.1, No.2 and No.3, respectively.
Fig. 6 The curves of the normalized autocorrelation function produced by the samples No.1, No.2 and No.3, respectively, and the extracted values of surface parameters.
The extracted results of samples are w1=0.41758±0.02854μm and ξ1=4.22302±0.6738μm for No.1; w2=0.46495±0.06494μmand ξ2=4.87313±0.4159μm for No.2; w3=0.57597±0.0358μm and ξ3=5.55361±0.63384μm for No.3, respectively. Comparing the results extracted from the autocorrelation function of speckles with those measured by AFM, we find that our method is of good accuracy.

As previously explained, the speckle patterns have the characteristics of fractals and are distributed in multi-scaled sizes. In order to deeply study the fractal features of speckles and make clear the fractal relationship between the speckles and the random surface, we can imitate the definition of the height autocorrelation function Rh(ρ) in Eq. (12) to define the autocorrelation function of speckle intensity as the following form:
γI(ρ)=exp[(ρ/ξI)2αI],
(15)
where ξI is the correlation length of speckles, and αIis the fractal exponent of the speckles. We note that the curves of 1γI(ρ) are linear in the range of ρ0 in the log-log scale, and the linear fitting will give the slope values of 2αI. The fitting results are shown in Figs. 7(a)-7(c) with the red lines, and the extracted fractal exponent of speckle patterns are αI1=0.7352±0.00436, αI2=0.7402±0.00449and αI3=0.7507±0.00522 for No.1-No.3, respectively.
Fig. 7 The curves of 1γI(ρ)vs.ρ produced by the samples No.1, No.2 and No.3, respectively, and the extracted fractal exponent αIof the speckle patterns. The insets show the probability density function curves of speckles for three samples.
With the fitting values of αI, we obtain the correlation length ξI of speckles for three surface samples by fitting the correlation curves of γI(ρ)vs.ρ in Figs. 6(a)-6(c) with Eq. (15). The corresponding results of three samples are respectively ξI1=0.59087±0.0039μm, ξI2=0.59386±0.0052μm, ξI3=0.59557±0.00412μm.

Comparing the fractal exponent αI of the speckle patterns with αof the random surfaces obtained from Eq. (14), we find that the fractal characteristic of speckle patterns is the same as that of the random surfaces, which can also be drawn from the expressions of Eqs. (14) and (15).

To demonstrate the circular Gaussian speckles are formed in our experimental condition, we have calculated the probability density function p(I/<I>) numerically with the intensity data of speckle patterns in Figs. 5(a)-5(c). The data of p(I/<I>) are shown in insets of Figs. 7(a)-7(c) in scattered square dot, and exponential decay fits are given in the red curves. We may see that the probability density curves in the small intensity region on the left of the blue dash line deviate from the exponential decay. This is induced by the background electrical noise with small amplitude in the detection of the intensity with the CCD [21

21. M. N. Zhang, Z. H. Li, X. Y. Chen, G. T. Liang, S. Y. Wang, S. Y. Teng, and C. F. Cheng, “Evolutions of speckles on rough glass/silver surfaces with film thickness,” Opt. Express 21(7), 8831–8843 (2013). [CrossRef] [PubMed]

]. On the whole, the probability density curves approach the negative exponential decay, and this indicates that the speckle fields could be taken as circular Gaussian speckles.

4. Discussion and conclusion

Acknowledgments

National Natural Science Foundation of China (Grant No. 10974122) and Shandong Provincial Natural Science Foundation, China (Grant No. ZR2013AM010)are gratefully acknowledged.

References and links

1.

P. Meakin, Fractal, Scaling and Growth Far From Equilibrium (Cambridge University, 1998).

2.

P. Córdoba-Torres, T. J. Mesquita, I. N. Bastos, and R. P. Nogueira, “Complex dynamics during metal dissolution: from intrinsic to faceted anomalous scaling,” Phys. Rev. Lett. 102(5), 055504 (2009). [CrossRef] [PubMed]

3.

S. Zhang, L. Wu, R. Y. Yue, Z. K. Yan, H. R. Zhan, and Y. Xiang, “Effects of Sb-doping on the grain growth of Cu(In, Ga)Se2 thin films fabricated by means of single-target sputtering,” Thin Solid Films 527, 137–140 (2013). [CrossRef]

4.

J. G. Goodberlet and H. Kavak, “Patterning sub-50 nm features with near-field embedded-amplitude masks,” Appl. Phys. Lett. 81(7), 1315–1317 (2002). [CrossRef]

5.

K. Jang, Y. Ishibashi, D. Iwata, H. Suganuma, T. Yamada, and Y. Takemura, “Fabrication of ferromagnetic nanoconstriction using atomic force microscopy nanoscratching,” J. Nanosci. Nanotechnol. 11(12), 10945–10948 (2011). [CrossRef] [PubMed]

6.

Ö. F. Farsakoğlu, D. M. Zengin, and H. Kocabaş, “Grinding process for beveling and lapping operations in lens manufacturing,” Appl. Opt. 39(10), 1541–1548 (2000). [CrossRef] [PubMed]

7.

J. C. Dainty, Laser Speckle and Related Phenomena 2nd edition (Springer Verlag, 1984).

8.

M. Giglio, M. Carpineti, and A. Vailati, “Space intensity correlations in the near field of the scattered light: a direct measurement of the density correlation function g(r),” Phys. Rev. Lett. 85(7), 1416–1419 (2000). [CrossRef] [PubMed]

9.

M. Giglio, M. Carpineti, A. Vailati, and D. Brogioli, “Near-field intensity correlations of scattered light,” Appl. Opt. 40(24), 4036–4040 (2001). [CrossRef] [PubMed]

10.

R. Cerbino, “Correlations of light in the deep Fresnel region: An extended Van Cittert and Zernike theorem,” Phys. Rev. A 75(5), 053815 (2007). [CrossRef]

11.

D. Brogioli, A. Vailati, and M. Giglio, “Heterodyne near-field scattering,” Appl. Phys. Lett. 81(22), 4109–4111 (2002). [CrossRef]

12.

C. F. Cheng, M. Liu, N. Y. Zhang, S. Y. Teng, H. S. Song, and Z. Z. Xu, “Speckle intensity correlation in the diffraction region near rough surfaces and simulational experiments for extraction of surface parameters,” Europhys. Lett. 65(6), 779–784 (2004). [CrossRef]

13.

G. T. Liang, X. Li, M. N. Zhang, Z. H. Li, C. X. Liu, and C. F. Cheng, “Experimental extraction of rough surface parameters from speckles in the deep Fresnel region with a scanning fibre-optic probe,” Eur. Phys. J. D 67, 030498 (2013).

14.

J. A. Ogilvy, Theory of Wave Scattering from Rough Surfaces (Adam Hilger, 1991).

15.

J. C. Dainty, The Statistics of Speckle Patterns, in Progress in Optics Vol. XIV, (North-Holland, 1976).

16.

J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications (Ben Roberts & Company, 2007).

17.

Y. P. Zhao, G. C. Wang, and T. H. Lu, Characterization of Amorphous and Crystalline Rough Surfaces: Principles and Applications (Academic, 2001).

18.

D. P. Qi, D. L. Liu, S. Y. Teng, N. Y. Zhang, and C. F. Cheng, “Morphology analysis atomic force microscope and light scattering study for random scattering screens,” Acta Phys. Sin. 49(7), 1260–1266 (2000).

19.

Y. P. Zhao, I. Wu, C. F. Cheng, U. Block, G. C. Wang, and T. M. Lu, “Characterization of random rough surfaces by in-plane light scattering,” J. Appl. Phys. 84(5), 2571–2582 (1998). [CrossRef]

20.

C. F. Cheng, C. X. Liu, S. Y. Teng, N. Y. Zhang, and M. Liu, “Half-width of intensity profiles of light scattered from self-affine fractal random surfaces and simulational verifications,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 65(6), 061104 (2002). [CrossRef] [PubMed]

21.

M. N. Zhang, Z. H. Li, X. Y. Chen, G. T. Liang, S. Y. Wang, S. Y. Teng, and C. F. Cheng, “Evolutions of speckles on rough glass/silver surfaces with film thickness,” Opt. Express 21(7), 8831–8843 (2013). [CrossRef] [PubMed]

OCIS Codes
(030.6140) Coherence and statistical optics : Speckle
(120.5820) Instrumentation, measurement, and metrology : Scattering measurements
(290.0290) Scattering : Scattering

ToC Category:
Scattering

History
Original Manuscript: December 6, 2013
Revised Manuscript: January 4, 2014
Manuscript Accepted: January 5, 2014
Published: January 13, 2014

Virtual Issues
Vol. 9, Iss. 3 Virtual Journal for Biomedical Optics

Citation
Chunxiang Liu, Qingrui Dong, Haixia Li, Zhenhua Li, Xing Li, and Chuanfu Cheng, "Measurement of surface parameters from autocorrelation function of speckles in deep Fresnel region with microscopic imaging system," Opt. Express 22, 1302-1312 (2014)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-22-2-1302


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. P. Meakin, Fractal, Scaling and Growth Far From Equilibrium (Cambridge University, 1998).
  2. P. Córdoba-Torres, T. J. Mesquita, I. N. Bastos, R. P. Nogueira, “Complex dynamics during metal dissolution: from intrinsic to faceted anomalous scaling,” Phys. Rev. Lett. 102(5), 055504 (2009). [CrossRef] [PubMed]
  3. S. Zhang, L. Wu, R. Y. Yue, Z. K. Yan, H. R. Zhan, Y. Xiang, “Effects of Sb-doping on the grain growth of Cu(In, Ga)Se2 thin films fabricated by means of single-target sputtering,” Thin Solid Films 527, 137–140 (2013). [CrossRef]
  4. J. G. Goodberlet, H. Kavak, “Patterning sub-50 nm features with near-field embedded-amplitude masks,” Appl. Phys. Lett. 81(7), 1315–1317 (2002). [CrossRef]
  5. K. Jang, Y. Ishibashi, D. Iwata, H. Suganuma, T. Yamada, Y. Takemura, “Fabrication of ferromagnetic nanoconstriction using atomic force microscopy nanoscratching,” J. Nanosci. Nanotechnol. 11(12), 10945–10948 (2011). [CrossRef] [PubMed]
  6. Ö. F. Farsakoğlu, D. M. Zengin, H. Kocabaş, “Grinding process for beveling and lapping operations in lens manufacturing,” Appl. Opt. 39(10), 1541–1548 (2000). [CrossRef] [PubMed]
  7. J. C. Dainty, Laser Speckle and Related Phenomena 2nd edition (Springer Verlag, 1984).
  8. M. Giglio, M. Carpineti, A. Vailati, “Space intensity correlations in the near field of the scattered light: a direct measurement of the density correlation function g(r),” Phys. Rev. Lett. 85(7), 1416–1419 (2000). [CrossRef] [PubMed]
  9. M. Giglio, M. Carpineti, A. Vailati, D. Brogioli, “Near-field intensity correlations of scattered light,” Appl. Opt. 40(24), 4036–4040 (2001). [CrossRef] [PubMed]
  10. R. Cerbino, “Correlations of light in the deep Fresnel region: An extended Van Cittert and Zernike theorem,” Phys. Rev. A 75(5), 053815 (2007). [CrossRef]
  11. D. Brogioli, A. Vailati, M. Giglio, “Heterodyne near-field scattering,” Appl. Phys. Lett. 81(22), 4109–4111 (2002). [CrossRef]
  12. C. F. Cheng, M. Liu, N. Y. Zhang, S. Y. Teng, H. S. Song, Z. Z. Xu, “Speckle intensity correlation in the diffraction region near rough surfaces and simulational experiments for extraction of surface parameters,” Europhys. Lett. 65(6), 779–784 (2004). [CrossRef]
  13. G. T. Liang, X. Li, M. N. Zhang, Z. H. Li, C. X. Liu, C. F. Cheng, “Experimental extraction of rough surface parameters from speckles in the deep Fresnel region with a scanning fibre-optic probe,” Eur. Phys. J. D 67, 030498 (2013).
  14. J. A. Ogilvy, Theory of Wave Scattering from Rough Surfaces (Adam Hilger, 1991).
  15. J. C. Dainty, The Statistics of Speckle Patterns, in Progress in Optics Vol. XIV, (North-Holland, 1976).
  16. J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications (Ben Roberts & Company, 2007).
  17. Y. P. Zhao, G. C. Wang, and T. H. Lu, Characterization of Amorphous and Crystalline Rough Surfaces: Principles and Applications (Academic, 2001).
  18. D. P. Qi, D. L. Liu, S. Y. Teng, N. Y. Zhang, C. F. Cheng, “Morphology analysis atomic force microscope and light scattering study for random scattering screens,” Acta Phys. Sin. 49(7), 1260–1266 (2000).
  19. Y. P. Zhao, I. Wu, C. F. Cheng, U. Block, G. C. Wang, T. M. Lu, “Characterization of random rough surfaces by in-plane light scattering,” J. Appl. Phys. 84(5), 2571–2582 (1998). [CrossRef]
  20. C. F. Cheng, C. X. Liu, S. Y. Teng, N. Y. Zhang, M. Liu, “Half-width of intensity profiles of light scattered from self-affine fractal random surfaces and simulational verifications,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 65(6), 061104 (2002). [CrossRef] [PubMed]
  21. M. N. Zhang, Z. H. Li, X. Y. Chen, G. T. Liang, S. Y. Wang, S. Y. Teng, C. F. Cheng, “Evolutions of speckles on rough glass/silver surfaces with film thickness,” Opt. Express 21(7), 8831–8843 (2013). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited