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High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks.
Martínez-Nieto, Javier Alejandro; Medrano-Marqués, Nicolás; Sanz-Pascual, María Teresa; Calvo-López, Belén.
Afiliação
  • Martínez-Nieto JA; Electronics Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla 72840, Mexico. almartinez@unizar.es.
  • Medrano-Marqués N; Group of Electronic Design (GDE), University of Zaragoza, 50009 Zaragoza, Spain. nmedrano@unizar.es.
  • Sanz-Pascual MT; Electronics Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla 72840, Mexico. materesa@inaoep.mx.
  • Calvo-López B; Group of Electronic Design (GDE), University of Zaragoza, 50009 Zaragoza, Spain. becalvo@unizar.es.
Sensors (Basel) ; 19(8)2019 Apr 16.
Article em En | MEDLINE | ID: mdl-30995743
For current microelectronic integrated systems, the design methodology involves different steps that end up in the full system simulation by means of electrical and physical models prior to its manufacture. However, the higher the circuit complexity, the more time is required to complete these simulations, jeopardizing the convergence of the numerical methods and, hence, meaning that the reliability of the results are not guaranteed. This paper shows the use of a high-level tool based on Matlab to simulate the operation of an artificial neural network implemented in a mixed analog-digital CMOS process, intended for sensor calibration purposes. The proposed standard tool enables modification of the neural model architecture to adapt its characteristics to those of the electronic system, resulting in accurate behavioral models that predict the complete microelectronic IC system behavior under different operation conditions before its physical implementation with a simple, time-efficient, and reliable solution.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: México País de publicação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: México País de publicação: Suíça