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into useful circuits [397]. Some areas of interest in nanotechnology include the following [208] (also see Table 6.2): (i) nanoengineering and nanodesign nanomachines, nanoCAD, nanodevices, nanoscale logic circuits; (ii) nanoelectronics molecular and computational nanoelectronics, nanodevices, electronic states, quantum dots, nanowires; (iii) nanomagnetism magnetic properties of nanostructures and nanostructured materials; and (iv) quantum computers theoretical aspects, computational methods for simulating quantum computers, devices, and algorithms. Nanoscientists working from the bottom up are now attempting to create a new understanding and structure from the dynamics of the basic materials and their molecules (the physical disciplines); those working from the top down seek to improve existing devices, such as transistors, and to make them smaller (engineering disciplines) [22]. Nanoscale metal and semiconductor particles exhibit a transition between quantum and bulk properties. When electrons become con ned in the mesoscopic regime, they display quantum mechanical behavior. A mesoscopic scale is not as small as a single atom, but small enough so that properties are signi cantly di erent from those in a microscopic piece of a material. Speci cally, nanoscale electronic components are governed by the quantum theory: with decreasing particle size, bulk properties

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Source: Report TID-25951, 1973.

PRIMARY KEY (UPC),

where this is measured along the x axis (Gaskill, 1978) The vertical Nyquist frequency is often different and is dependent on the vertical pixel pitch The reasoning behind this limit is that to measure a fringe of a particular frequency or period, at least two samples per period must be measured, ie we must be capable of measuring the high and low points on a fringe If the fringe frequency exceeds the Nyquist frequency, the aliasing results of this sampled interferogram are not interpretable by standard PSI techniques Aliasing and methods to get around this limitation are discussed in Section 1413 1411 QUALITY FUNCTIONS There is one more operation that must be performed to the calculated phase f x; y before it is ready to be displayed and evaluated.

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In Section 5.11, examples of linear expectation models were presented. In this section, a general expression is derived for the least squares estimator of the parameters of such models. The least squares criterion chosen is the general weighted least squares criterion described by (5.173) and (5.174): (5.218)

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We must correct for the discontinuities that occur in the phase calculation as a result of the arctangent, since the standard arctangent is de ned only over the limited range of angles, p=2 to p=2 Regardless of the actual value of the phase, only values of the phase within this limited result from Eq (1427), or its equivalent for other algorithms This limitation might appear to restrict our ability to measure OPDs longer than a half a wavelength Fortunately, there is suf cient information in the calculation to remedy the situation and provide a usable extended measurement range In addition, we know that the wavefront or surface is in fact continuous and can extend over a much larger range The rst correction to be made to the calculated phase is to extend the calculation range from 0 to 2p.

XTC-'d(t)

This is possible because the signs of the sine and cosine are known independently of the sign of the tangent Eq (1424) and (1425), for the four step algorithm, are directly proportional to the sine and the cosine Similar relationships exist for the other algorithms that are discussed in this chapter Table 1411 gives the formulas needed to convert the arctangent results to values between 0 and 2p as a function of the values of the sine and the cosine, and this process is shown graphically in Figure 146 The result of this correction is to produce the wavefront phase modulo 2p; every time the actual phase equals a multiple of 2p, the calculated.

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= (w1 . . . W N ) ~are assumed to possess expectations E w = p = g(0), where 6 is a K x 1 vector of parameters m with K < N . Let q(w; ) be the log-likelihoodfunction of the expectation parameters m with elements corresponding to those of p. Define f i as the maximum likelihood estimate o p and as the maximum likelihood estimate of 6,respectively. Then, the log-likelihood f ratio for the assumed expectation model is described by

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