User Guide

Terms You Need to Understand 1
-5
Terms You Need to
Understand
Optimization
Optimization is the process of fine-tuning a
design by varying design parameters between successive
simulations until performance comes close to (or exactly meets) the
ideal performance.
The PSpice Optimizer solves four types of optimization problems
as described in Table 1-1.
*. All four cases allow simple bound constraints; that is, lower and upper bounds on all of
the parameters. The PSpice Optimizer also handles nonlinear goals and constraints.
**. Use unconstrained least squares when fitting model parameters to a set of
measurements, or when minimizing more than one goal.
Table 1-1
Optimization Problems
*
Problem Type PSpice Optimizer Action Example
unconstrained minimization reduces the value of a single goal minimize the propagation delay
through a logic cell
constrained minimization reduces the value of a single goal while
satisfying one or more constraints
minimize the propagation delay
through a logic cell while keeping the
power consumption of the cell less than
a specified value
unconstrained least squares
**
reduces the sum of the squares of the
individual errors (difference between
the ideal and the measured value) for a
set of goals
given a terminator design, minimize
the sum of squares of the errors in
output voltage and equivalent
resistance
constrained least squares reduces the sum of squares of the
individual errors for a set of goals
while satisfying one or more
constraints
minimize the sum of squares of the
figures of merit for an amplifier design
while keeping the open loop gain equal
to a specified value