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Unstable results for heavily doped Ge nanodisk arrays #7
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Hi Bjorn Hope everything is going fine with your thesis. I have a small update on the subject. Correctness of the results is a function of the parameter max_order_PWs, and as a consequence of the number of bloch orders. For high (greater than 3) max_order_PWs the result becomes wrong and unphysical, yet stable from run to run. It seems to me that all the bloch modes are correctly computed if i take a look at the plots of the Bloch fields, and indeed I get no error or warning from the code. Defining a finer mesh (even a very fine one) does not seem to help. The problem could lie in the enforcing of the correct boundary conditions: may be that overdetermined linear system to obtain the correct boundary conditions is not correctly solved when it becomes too large. Let me know what you think about it. Best Giovanni |
Hi Giovanni, I handed in my thesis on Friday, so I will have a bit of time to look into these issues! I'm looking forward to clearing these issue up! |
Hi Bjorn Thanks for the answer. I will run my calculations from a clean repo and see what happens. Best Giovanni |
Hi Bjorn I run it from a clean copy of the repo with |
Hi Giovanni, can you specify what libraries and compilers you are using? I haven't Cheers Kind Regards, Björn Sturmberg On Thu, Nov 26, 2015 at 2:55 AM, Giovanni Pellegrini <
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Hi Bjorn I performed some test and below are reported all the relevant informations:
For the moment this is all Best Giovanni |
I am working on heavily doped Ge nanodisk array 500nm high and 1000nm of diameter. Except for the fact that I am working in the infrared [5000-20000]nm, everything is as for simple plasmonic nanostructures in the visible.
What happens is as seen in this jupyter notebook. In spite of a mesh that seems to be more than fair, with respect to the spatial variations of the field, the output is extremely sensitive to mesh parameters, and there seem to be no definite rule to get a stable output.
In this particular case I run the notebook with a modified EMUstack version (python3 compatible), but i checked with the as is python2 version and the outputs are identical.
The fact is that the results are nonphysical but not random, i.e. they are incorrect but identical run after run. Do you have any suggestion for me? I was planning to couple EMUstack with a genetic optimizer, but as long as the output is not stable for a reasonable set of parameters, this is going to be difficult for me.
Best
Giovanni
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