By Helmut E. Graeb
What you’ll locate here's a interesting compendium of basic challenge formulations of analog layout centering and sizing. This crucial paintings offers a differentiated wisdom concerning the projects of analog layout centering and sizing. specifically, worst-case situations are formulated and analyzed. This paintings is true on the crossing element among strategy and layout expertise, and is either reference paintings and textbook for figuring out CAD equipment in analog sizing.
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Extra info for Analog Design Centering and Sizing
If two parameters xs,k , xs,l are perfectly correlated, then | k,l | = 1. Perfect correlation refers to a functional relationship between the two parameters. e. the statistical uncertainty between the two parameters disappears. e. (23) cannot be written. In the following, we will assume that | k,l | <1 (33) This means that C is positive definite and that the multivariate normal distribution is nonsingular. Asingular distribution can be transformed into a nonsingular one . e. e. e. the equidensity curves, are determined by the quadratic form (24).
8, and let the correlation between R and C be = 0. 5 + C−C 1 σR σC pdf(R, C) = ·e (5) 2πσR σC A probability density function is the continuous counterpart to a relative frequency distribution. A relative frequency distribution describes the percentage of events in each class of parameter values among all events. For a probability density function this corresponds to the probability of occurrence in an infinitesimal parameter class (R + dR) · (C + dC), which is given by pdf(R, C) · (R + dR) · (C + dC).
E. z(y) and y(z), the probability density function pdfz of random variable z is obtained from the probability density function pdfy of random variable y by: pdfz (z) = pdfy (y(z)) · det pdfz (z) = pdfy (y(z)) · ∂y ∂zT (39) ∂y ∂z The idea behind the transformation is to keep the probability of an infinitesimal parameter interval equal while transforming it to another infinitesimal interval: pdfz (z) · dz = pdfy (y) · dy (40) This corresponds to a substitution of variable y in the integration over pdfy to calculate cdfy (13): y1 cdfy (y) = −∞ yny ...