Waves and Optics by Paul Padley - HTML preview

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Chapter 11Fourier Optics

11.1Fourier Optics*

Fourier Transforms

The Fourier Transform can be used to represent any well behaved function f ( x ) .

_autogen-svg2png-0002.png where A ( k ) = ∫ − ∞ f ( x ) cos ( kx ) ⅆ x B ( k ) = ∫ − ∞ f ( x ) sin ( kx ) ⅆ x I can now substitute for A and B in the original expression and write:

_autogen-svg2png-0007.png and then use cos ( xx ) = c o s k x cos k x + sin kx sin k x

_autogen-svg2png-0009.png Since the inner integral is an even function we can write _autogen-svg2png-0010.png Now consider the fact that _autogen-svg2png-0011.png because sin is an odd function, ie. − ∞ sin ( k [ xx ] ) ⅆ k = 0 So we could have written _autogen-svg2png-0014.png or _autogen-svg2png-0015.png or _autogen-svg2png-0016.png where g ( k ) = ∫ − ∞ f ( x ) eikxx is the Fourier transform of f ( x ) .

Symbolically we write g ( k ) = Ϝ { f ( x ) } f ( x ) = Ϝ − 1 { g ( k ) } = Ϝ − 1 { Ϝ { f ( x ) } }

Now these concepts are easily extended to two dimensions:

_autogen-svg2png-0021.png where g ( kx , ky ) = ∫ − ∞ − ∞ f ( x , y ) ei ( x kx + y ky ) xy .

This tells us is that any nonperiodic function of two variables f ( x , y ) can be synthesized from a distribution of plane waves each with amplitude g ( kx , ky ) .

Lets consider Fraunhofer diffraction through an aperture again. For example consider a rectangular aperture as show in the figure. If _autogen-svg2png-0025.png is the source strength per unit area (assumed to be constant over the entire area in this example) and dS = ⅆ xz is an infinitesmal area at a point in the aperture then we have:

Figure (graphics/RectangularAperture.png)
Figure 11.1

We can define a source strength per unit area _autogen-svg2png-0027.png

Notice that I flipped the sign in the exponential from what I used in the earlier lectures on diffraction. This does not change the physics content of what we are doing in any way, however it allows our notation to follow standard convention. _autogen-svg2png-0028.png

If we define ky = k Y / R and kz = Z / R and we see that

_autogen-svg2png-0031.png

That is, it is equal to the Fourier transform. In fact one can define an "Aperture Function" A ( y , z ) . Such that

_autogen-svg2png-0033.png

For a rectangular aperture _autogen-svg2png-0034.png inside the aperture and zero outside it. The aperture function can be much more complex (literally) allowing for changes in source strength and phase through the aperture. The resulting E field is the Fourier transform of the aperture function.

11.2Dirac Delta Function*

The above allows us to relate the Fourier transform of an Aperture and the resulting E field from diffraction through that aperture. To extend this to an array of apertures, requires that one introduce a new concept, the Dirac delta function.

The fundamental definition of the Dirac delta function is

_autogen-svg2png-0001.png

As a special case if f ( x ) = 1

− ∞ δ ( x ) ⅆ x = 1

This function has some important properties:

f ( x ) = ∫ − ∞ f ( x ) δ ( xx ) ⅆ x which follows direction from the definition ie. define a new coordinate a = xx , then x = a + x and da = ⅆ x . Then

_autogen-svg2png-0008.png

We also note that δ ( x ) = δ ( − x ) .

It gets even more interesting. Recall_autogen-svg2png-0010.png which we rearrange suggestively_autogen-svg2png-0011.png we also have f ( x ) = ∫ − ∞ f ( x ) δ ( xx ) ⅆ x so we must have_autogen-svg2png-0013.png or_autogen-svg2png-0014.png

That is the Dirac delta function is the inverse Fourier transform of 1. This is a very useful property that allows us to do problems like Young's double slit. Consider the aperture function: A = E0 ( δ ( ya / 2 ) + δ ( y + a / 2 ) ) where we have represented the slits by Dirac delta functions. Then we obtain _autogen-svg2png-0016.png which is exactly what we obtained before. Well that was a lot easier than what we did earlier in the course!

11.3The Convolution Theorem and Diffraction*

To handle more complex cases of diffraction using Fourier transforms we need to know the convolution theorem. Say g ( x ) is the convolution of two other functions f and h . Then g ( x ) = fh = ∫ − ∞ f ( x ) h ( xx ) ⅆ xIt is probably best to illustrate convolution with some examples. In each example, the blue line represents the function h ( xx ) , the red line the function f ( x ) and the green line is the convolution. In the animation; follow the vertical green line that is the point where the convolution is being evaluated. Its value is the area under the product of the two curves at that point.

Figure (TwoRectAni.gif)
Figure 11.2

It might be easier to picture what is going on if we capture a couple of frames.

Figure (index_21.gif)
Figure 11.3

Figure (index_25.gif)
Figure 11.4

Here is a slightly more complicated example

Figure (AniGaussRect.gif)
Figure 11.5

Finally it is interesting to note what happens when we spread out a few functions, that is in this case, f is a step function in a couple of