Xyce™ Parallel Electronic Simulator Reference Guide (V.7.4)

Keiter, Eric R.; Russo, Thomas V.; Schiek, Richard; Thornquist, Heidi K.; Mei, Ting; Verley, Jason C.; Sholander, Peter E.; Aadithya, Karthik; Schickling, Joshua

doi:10.2172/1826862

Cited by 2 publications

(3 citation statements)

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“…-Xyce (Keiter et al, 2014) in DutXyce, and -Ngspice (Vogt, 2022) in DutNgspice. • DutTCAD ads logic for DUT instances that represents devices based on TCAD simulations.…”

Section: Discussionmentioning

confidence: 99%

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DMT-core: A Python Toolkit for Semiconductor Device Engineers

Krattenmacher¹,

Müller²,

Kuthe³

et al. 2022

JOSS

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Semiconductor device engineers are faced by a number of non-trivial tasks that can be solved efficiently using software. These tasks include, amongst others, data analysis, visualization and processing, as well as interfacing various circuit and Technology-Computer-Aided-Design (TCAD) simulators. In practice, custom 'home-made' scripts of varying quality are employed to solve these tasks. It is often found that fundamental software engineering concepts, such as Test-Driven-Development (Shull et al., 2010), or the use of state-of-the-art version control tools (e.g. Git) and practices (e.g. continuous integration, CI), are not utilized by these scripts.

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“…-Xyce (Keiter et al, 2014) in DutXyce, and -Ngspice (Vogt, 2022) in DutNgspice. • DutTCAD ads logic for DUT instances that represents devices based on TCAD simulations.…”

Section: Discussionmentioning

confidence: 99%

“…• classes and methods relevant to commonly used device engineering tasks • several abstract base classes for implementing new interfaces to various types of simulators • concrete implementations of the abstract base classes for open-source simulators such as Ngspice (Vogt, 2022), Xyce (Keiter et al, 2014) or Hdev (Müller et al, 2022).…”

mentioning

confidence: 99%

DMT-core: A Python Toolkit for Semiconductor Device Engineers

Krattenmacher¹,

Müller²,

Kuthe³

et al. 2022

JOSS

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show abstract

“…To the author's knowledge, circuit solvers, such as Xyce, have no such built-in methods to increase their computational efficiency. Instead, for such large-scale problems they suggest the user rely on parallel computing and iterative methods, such as Krylov, which require a well-designed and problemspecific preconditioner for good performance [9]- [11]. Here, we present a much simpler and more astute alternative to such complicated algorithms for reducing the computational intensity of modeling a large mesh network of photovoltaic pixels connected via a common electrolyte.…”

mentioning

confidence: 99%

Accelerated Simulation of Multi-Electrode Arrays Using Sparse and Low-Rank Matrix Techniques

Jensen,

Chen,

Kochnev Goldstein

et al. 2024

Preprint

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Modeling of Multi-Electrode Arrays used in neural stimulation can be computationally challenging since it may involve incredibly dense circuits with millions of interconnected resistors, representing current pathways in an electrolyte (resistance matrix), coupled to nonlinear circuits of the stimulating pixels themselves. Here, we present a method for accelerating the modeling of such circuits while minimizing the error of a simplified simulation by using a sparse plus low-rank approximation of the resistance matrix. Specifically, we prove that thresholding of the resistance matrix elements enables its sparsification with minimized error. This is accomplished with a sorting algorithm implying efficient O(Nlog(N)) complexity. The eigenvalue-based low-rank compensation then helps achieve greater accuracy without adding significantly to the problem size. Utilizing these matrix techniques, we accelerated the simulation of multi-electrode arrays by an order of magnitude, reducing the computation time by about 10-fold, while maintaining an average error of less than 0.3% in the current injected from each electrode. We also show a case where acceleration reaches at least 133 times with additional error in the range of 4%, demonstrating the ability of this algorithm to perform under extreme conditions. Although the techniques presented here are used for simulations of photovoltaic retinal prostheses, they are also immediately applicable to any circuit involving dense connections between nodes, and, with modifications, more generally to any systems involving non-sparse matrices. This approach promises significant improvements in the efficiency of modeling the next-generation retinal implants having thousands of pixels, enabling iterative design with broad applicability.

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Xyce™ Parallel Electronic Simulator Reference Guide (V.7.4)

Cited by 2 publications

References 0 publications

DMT-core: A Python Toolkit for Semiconductor Device Engineers

DMT-core: A Python Toolkit for Semiconductor Device Engineers

Accelerated Simulation of Multi-Electrode Arrays Using Sparse and Low-Rank Matrix Techniques

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