Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks
  with 3D EM Structures: A Study on A Transformer-Based Matching Network   This paper presents a deep learning assisted synthesis approach for direct
end-to-end generation of RF/mm-wave passive matching network with 3D EM
structures. Different from prior approaches that synthesize EM structures from
target circuit component values and target topologies, our proposed approach
achieves the direct synthesis of the passive network given the network topology
from desired performance values as input. We showcase the proposed synthesis
Neural Network (NN) model on an on-chip 1:1 transformer-based impedance
matching network. By leveraging parameter sharing, the synthesis NN model
successfully extracts relevant features from the input impedance and load
capacitors, and predict the transformer 3D EM geometry in a 45nm SOI process
that will match the standard 50$\Omega$ load to the target input impedance
while absorbing the two loading capacitors. As a proof-of-concept, several
example transformer geometries were synthesized, and verified in Ansys HFSS to
provide the desired input impedance.

17
The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
2
The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
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The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat