High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks
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 simul...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/8/1814 |
| _version_ | 1811299615649038336 |
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| author | Javier Alejandro Martínez-Nieto Nicolás Medrano-Marqués María Teresa Sanz-Pascual Belén Calvo-López |
| author_facet | Javier Alejandro Martínez-Nieto Nicolás Medrano-Marqués María Teresa Sanz-Pascual Belén Calvo-López |
| author_sort | Javier Alejandro Martínez-Nieto |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T06:38:22Z |
| format | Article |
| id | doaj.art-6818ab9aa79e4593a1921ae8d804f773 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:38:22Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6818ab9aa79e4593a1921ae8d804f7732022-12-22T02:57:49ZengMDPI AGSensors1424-82202019-04-01198181410.3390/s19081814s19081814High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural NetworksJavier Alejandro Martínez-Nieto0Nicolás Medrano-Marqués1María Teresa Sanz-Pascual2Belén Calvo-López3Electronics Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla 72840, MexicoGroup of Electronic Design (GDE), University of Zaragoza, 50009 Zaragoza, SpainElectronics Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla 72840, MexicoGroup of Electronic Design (GDE), University of Zaragoza, 50009 Zaragoza, SpainFor 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.https://www.mdpi.com/1424-8220/19/8/1814Artificial Neural NetworksCMOS ASICsembedded systemscircuit simulationhigh-level modeling |
| spellingShingle | Javier Alejandro Martínez-Nieto Nicolás Medrano-Marqués María Teresa Sanz-Pascual Belén Calvo-López High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks Sensors Artificial Neural Networks CMOS ASICs embedded systems circuit simulation high-level modeling |
| title | High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks |
| title_full | High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks |
| title_fullStr | High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks |
| title_full_unstemmed | High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks |
| title_short | High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks |
| title_sort | high level modeling and simulation tool for sensor conditioning circuit based on artificial neural networks |
| topic | Artificial Neural Networks CMOS ASICs embedded systems circuit simulation high-level modeling |
| url | https://www.mdpi.com/1424-8220/19/8/1814 |
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