| 成龙 |
2009-09-15 14:42 |
How Many Rays Do I Need for Monte Carlo Optimization? ~;-2eKw
While it is important to ensure that a sufficient number of rays are traced to h+7U'+|%A
distinguish the merit function value from the noise floor, it is often not necessary to \Una wv~
trace as many rays during optimization as you might to obtain a given level of XZQ-Ig18
accuracy for analysis purposes. What matters during optimization is that the $2Awp@j
changes the optimizer makes to the model affect the merit function in the same way &> _aY #
that the overall performance is affected. It is possible to define the merit function so w#_7,*6 ]
that it has less accuracy and/or coarser mesh resolution than meshes used for QCG-CzJ9 l
analysis and yet produce improvements during optimization, especially in the early :#\jx
stages of a design. JvEW0-B^l,
A rule of thumb for the first Monte Carlo run on a system is to have an average of at 9=FH2|Z
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays -7XaS&.4
on the receiver to achieve uniform distribution. It is likely that you will need to O$x +>^
define more rays than 800 in a simulation in order to get 800 rays on the receiver. ] T(qk
When using simplified meshes as merit functions, you should check the before and 4]E1x l
after performance of a design to verify that the changes correlate to the changes of mI\[L2x
the merit function during optimization. As a design reaches its final performance ADM!4L(s4}
level, you will have to add rays to the simulation to reduce the noise floor so that Uu@qS
sufficient accuracy and mesh resolution are available for the optimizer to find the C?MKb D=K
best solution.
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