000079634 001__ 79634
000079634 005__ 20200716101445.0
000079634 0247_ $$2doi$$a10.3390/s19081814
000079634 0248_ $$2sideral$$a112236
000079634 037__ $$aART-2019-112236
000079634 041__ $$aeng
000079634 100__ $$aMartinez-Nieto, Javier Alejandro
000079634 245__ $$aHigh-level modeling and simulation tool for sensor conditioning circuit based on artificial neural networks
000079634 260__ $$c2019
000079634 5060_ $$aAccess copy available to the general public$$fUnrestricted
000079634 5203_ $$aFor 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.
000079634 536__ $$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/TEC2015-65750-R
000079634 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000079634 590__ $$a3.275$$b2019
000079634 591__ $$aCHEMISTRY, ANALYTICAL$$b22 / 86 = 0.256$$c2019$$dQ2$$eT1
000079634 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b15 / 64 = 0.234$$c2019$$dQ1$$eT1
000079634 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b77 / 266 = 0.289$$c2019$$dQ2$$eT1
000079634 592__ $$a0.653$$b2019
000079634 593__ $$aInstrumentation$$c2019$$dQ1
000079634 593__ $$aAtomic and Molecular Physics, and Optics$$c2019$$dQ2
000079634 593__ $$aMedicine (miscellaneous)$$c2019$$dQ2
000079634 593__ $$aInformation Systems$$c2019$$dQ2
000079634 593__ $$aAnalytical Chemistry$$c2019$$dQ2
000079634 593__ $$aElectrical and Electronic Engineering$$c2019$$dQ2
000079634 593__ $$aBiochemistry$$c2019$$dQ3
000079634 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000079634 700__ $$0(orcid)0000-0002-5380-3013$$aMedrano-Marques, Nicolás$$uUniversidad de Zaragoza
000079634 700__ $$aSanz-Pascual, María Teresa
000079634 700__ $$0(orcid)0000-0003-2361-1077$$aCalvo-Lopez, Belén$$uUniversidad de Zaragoza
000079634 7102_ $$15008$$2250$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Electrónica
000079634 773__ $$g19, 8 (2019), [25 pp.]$$pSensors$$tSensors (Switzerland)$$x1424-8220
000079634 8564_ $$s7055137$$uhttps://zaguan.unizar.es/record/79634/files/texto_completo.pdf$$yVersión publicada
000079634 8564_ $$s105782$$uhttps://zaguan.unizar.es/record/79634/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000079634 909CO $$ooai:zaguan.unizar.es:79634$$particulos$$pdriver
000079634 951__ $$a2020-07-16-09:02:40
000079634 980__ $$aARTICLE