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Sonnet User's Guide - Sonnet Software

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Chapter 10 Parameterizing your Project<br />

inal values are in the right value range when the optimizer is started. Otherwise,<br />

the optimizer may converge to a local minima for which the error is not the minimum<br />

achievable value, as pictured below.<br />

Error<br />

Function<br />

Local Minima<br />

Parameter Values<br />

Actual Minimum<br />

You specify a goal by identifying a particular measurement and what value you<br />

desire it to be. For example S 11 < -20 dB. Keep in mind that the goals you specify<br />

may not be possible to satisfy. Em finds the solution with the least error.<br />

You may also specify a goal by equating a measurement in one network to a measurement<br />

in another network or file. For example, you may set S 11 for network<br />

“Model” equal to S 11 for network “Measured.” Likewise, you may equate S 11 for<br />

network “Model” to S 11 for data file “meas.s2p.”<br />

You may select one or multiple variables to optimize. For each variable that you<br />

select you must specify minimum and maximum bounds. The analysis limits the<br />

variables to values within the specified bounds.<br />

Variables that are used in an optimization have a granularity value assigned to<br />

them; the granularity defines the finest resolution, the smallest interval between<br />

values, of a variable for which em will do a full electromagnetic simulation during<br />

optimization. For values which occur between those set by this resolution, em performs<br />

an interpolation to produce the analysis data. By default, the software determines<br />

the granularity, but you may enter a value manually.<br />

You specify the number of iterations. For each iteration, em selects a value for<br />

each of the variables included in the optimization, then analyzes the circuit at each<br />

frequency specified in the goals. Depending on the complexity of the circuit, the<br />

number of analysis frequencies and the number of variable combinations, an optimization<br />

may take a significant amount of processing time. The number of iterations<br />

provides a measure of control over the process. Note that the number of<br />

iterations is a maximum. An optimization can stop after fewer iterations if the optimization<br />

goal is achieved or it finds a minima (finds no improvement in the error<br />

in further iterations).<br />

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