Resource optimization for emulation of behavioral models of mixed signal circuits on FPGA: a case study of DC-DC buck converter

Abstract This paper uses an interesting case study to highlight high-resolution pulsed thermal-induced voltage alteration (TIVA) with solid immersion lens (SIL) as a technique to isolate a temperature-sensitive failure in mixed-signal circuitry, followed by circuit analysis and nanoprobing to confirm a drive strength issue caused by a process change.

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Analysis and testing of analog and mixed-signal circuits by an operation-region model: a case study of application and implementation

Proceedings. 16th IEEE Symposium on Computer Arithmetic ◽

10.1109/dftvs.2003.1250122 ◽

2004 ◽

Cited By ~ 7

Author(s):

Y. Miura ◽

D. Kato

Keyword(s):

Mixed Signal ◽

Mixed Signal Circuits ◽

Region Model

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Fault diagnosis in switched-linear systems by emulation of behavioral models on FPGA: A case study of current-mode buck converter

International Journal of Numerical Modelling Electronic Networks Devices and Fields ◽

10.1002/jnm.2314 ◽

2017 ◽

Vol 31 (5) ◽

pp. e2314

Author(s):

Rahul Bhattacharya ◽

Subindu Kumar ◽

Santosh Biswas

Keyword(s):

Fault Diagnosis ◽

Linear Systems ◽

Current Mode ◽

Buck Converter ◽

Behavioral Models ◽

Switched Linear Systems

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Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Energies ◽

10.3390/en14123385 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3385

Author(s):

Erickson Puchta ◽

Priscilla Bassetto ◽

Lucas Biuk ◽

Marco Itaborahy Filho ◽

Attilio Converti ◽

...

Keyword(s):

Pid Controller ◽

Optimization Algorithms ◽

Optimization Methods ◽

Buck Converter ◽

Algebraic Solution ◽

Nonlinear Load ◽

Bee Colony ◽

Arduous Task ◽

Whale Optimization

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

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