Algorithms for Analysis of Nonlinear High-Frequency Circuits
Type of document
disertační práceAuthor
Černý, David
Supervisor
Dobeš, Josef
Field of study
RadioelektronikaStudy program
Elektrotechnika a informatikaInstitutions assigning rank
České vysoké učení technické v Praze. Fakulta elektrotechnická. Katedra radioelektronikyMetadata
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The most efficient simulation solvers use composite procedures that adaptively rearrange
computation algorithms to maximize simulation performance. Fast and stable processing
optimized for given simulation problem is essential for any modern simulator. It is
characteristic for electronic circuit analysis that complexity of simulation is affected
by circuit size and used device models. Implementation of electronic device models in
program SPICE uses traditional implementation allowing fast computation but further
modification of model can be questionable.
The first fundamental thesis aim is scalability of the simulation based on the adaptive
internal solver composing different algorithms according to properties of simulation
problem to maximize simulation performance. In a case of the small circuit as faster
solution prove simple, straightforward methods that utilize arithmetic operations without
unnecessary condition jumping and memory rearrangements that can not be effectively
optimized by a compiler. The limit of small size simulation problems is related to
computation machine capabilities. The present day PC sets this limit to fifty independent
voltage nodes where inefficiency of calculation procedure does not play any role in overall
processor performance. The scalable solver must also be able to handle correctly simulation
of large-scale circuits that requires entirely different approach apart to standard size
circuits. The unique properties of simulation of the electronic circuits that played until this
time only the minor role suddenly gain on significance for circuits with several thousand
voltage nodes. In those particular cases, iterative algorithms based on Krylov subspace
methods provide better results from the aspect of performance than standard direct
methods. This thesis also proposes unique techniques of indexation of the large-scale
sparse matrix system. The primary purpose is to reduce memory requirements for storing
sparse matrices during simulation computation.
The second fundamental thesis aim is automatic adaptivity of device models definition
respecting current simulation state and settings. This principle is denoted as Functional
Chaining mechanism that is based on the principle of the automatic self-modifying
procedure utilizing state-of-the-art functional computation layer during the simulation
process. It can significantly improve mapping performance of circuit variables to device
models; it also allows autonomous redefinition of simulation algorithms during analysis
with an intention to reduce computation time. The core idea is based on utilization of
programming principles related to functional programming languages. It is also presents
possibilites of reimplementation to the modern object-oriented languages.
The third fundamental thesis aim focuses on simulation accuracy and reliability. Arbitrary
precision variable types can directly lead to increased simulation accuracy but on
the other hand; they can significantly decrease simulation performance. In last chapters,
there are several algorithms provided with the claim to provide better simulation accuracy
and suppress computation errors of floating point data types.
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