{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:04:39Z","timestamp":1760709879792,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,16]],"date-time":"2019-04-16T00:00:00Z","timestamp":1555372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["CB-2015-257985"],"award-info":[{"award-number":["CB-2015-257985"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MINECO-FEDER, UE","award":["TEC2015-65750-R"],"award-info":[{"award-number":["TEC2015-65750-R"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19081814","type":"journal-article","created":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T03:02:01Z","timestamp":1555470121000},"page":"1814","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks"],"prefix":"10.3390","volume":"19","author":[{"given":"Javier Alejandro","family":"Mart\u00ednez-Nieto","sequence":"first","affiliation":[{"name":"Electronics Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla 72840, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5380-3013","authenticated-orcid":false,"given":"Nicol\u00e1s","family":"Medrano-Marqu\u00e9s","sequence":"additional","affiliation":[{"name":"Group of Electronic Design (GDE), University of Zaragoza, 50009 Zaragoza, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5820-7608","authenticated-orcid":false,"given":"Mar\u00eda Teresa","family":"Sanz-Pascual","sequence":"additional","affiliation":[{"name":"Electronics Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla 72840, Mexico"}]},{"given":"Bel\u00e9n","family":"Calvo-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Group of Electronic Design (GDE), University of Zaragoza, 50009 Zaragoza, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,16]]},"reference":[{"key":"ref_1","unstructured":"Lopez-Martin, A.J., and Carlosena, A. (March, January 27). Sensor signal linearization techniques: A comparative analysis. Proceedings of the IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS), Cusco, Peru."},{"key":"ref_2","unstructured":"Guerrero, E., Sanz-Pascual, M.T., Molina-Reyes, J., Medrano, N., and Calvo, B. (March, January 29). A digitally programmable calibration circuit for smart sensors. Proceedings of the IEEE 3rd Latin American Symposium on Circuits and Systems (LASCAS), Playa del Carmen, Mexico."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.snb.2007.07.144","article-title":"A portable integrated wide-range gas sensing system with smart A\/D front-end","volume":"130","author":"Baschirotto","year":"2008","journal-title":"Sens. Actuators B Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1049\/iet-smt.2018.5228","article-title":"T\u2013S fuzzy-based multi-LAE approach for sensor linearization","volume":"12","author":"Sarawade","year":"2018","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8256","DOI":"10.1109\/JSEN.2018.2856300","article-title":"Widening and Linearization of DC Magnetic Field Magnetoelectric Sensor Characteristic Using a Compensation Scheme","volume":"18","author":"Serov","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gao, Z., Zhou, B., Hou, B., Li, C., Wei, Q., and Zhang, R. (2018). Self-Calibration of Angular Position Sensors by Signal Flow Networks. Sensors, 18.","DOI":"10.3390\/s18082513"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.sna.2018.08.001","article-title":"An efficient signal conditioning circuit to piecewise linearizing the response characteristic of highly nonlinear sensors","volume":"280","author":"Mahaseth","year":"2018","journal-title":"Sens. Actuators A Phys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.sna.2018.08.033","article-title":"A CMOS full-range linear integrated interface for differential capacitive sensor readout","volume":"281","author":"Barile","year":"2018","journal-title":"Sens. Actuators A Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/S0893-6080(98)00122-1","article-title":"ANNSyS: An Analog Neural Network Synthesis System","volume":"12","author":"Bayraktaroglu","year":"1999","journal-title":"Neural Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/JSEN.2009.2033463","article-title":"Designing Adaptive Conditioning Electronics for Smart Sensing","volume":"10","author":"Zatorre","year":"2010","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1109\/TCSI.2008.916617","article-title":"Neural-Network-Based Robust Linearization and Compensation Technique for Sensors Under Nonlinear Environmental Influences","volume":"55","author":"Patra","year":"2008","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1109\/JSEN.2016.2631507","article-title":"Comments on \u201cDevelopment of an ANN-Based Linearization Technique for the VCO Thermistor Circuit\u201d","volume":"17","author":"Rana","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1109\/41.969414","article-title":"Sensor linearization with neural networks","volume":"48","year":"2001","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Teodorescu, H.L. (2017, January 13\u201314). Fuzzy logic system linearization for sensors. Proceedings of the International Symposium on Signals, Circuits and Systems (ISSCS), Iasi, Romania.","DOI":"10.1109\/ISSCS.2017.8034892"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Suszynski, R., Marciniak, J., and Wawryn, K. (2016, January 23\u201325). An artificial neural network for classification a quality of a coal fuel in combustion chambers using FPAA. Proceedings of the International Conference Mixed Design of Integrated Circuits and Systems, Lodz, Poland.","DOI":"10.1109\/MIXDES.2016.7529723"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tala\u015bka, T. (2018). Components of Artificial Neural Networks Realized in CMOS Technology to be Used in Intelligent Sensors in Wireless Sensor Networks. Sensors, 18.","DOI":"10.3390\/s18124499"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tala\u015bka, T. (2019). Parallel, asynchronous, fuzzy logic systems realized in CMOS technology. Adv. Comput. Math.","DOI":"10.1007\/s10444-018-09659-5"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.neucom.2016.06.014","article-title":"Neural networks: An overview of early research, current frameworks and new challenges","volume":"214","author":"Prieto","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.isatra.2010.04.004","article-title":"Implementation of software-based sensor linearization algorithms on low-cost microcontrollers","volume":"49","author":"Erdem","year":"2010","journal-title":"ISA Trans."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1016\/j.amc.2018.07.053","article-title":"On artificial neural networks approach with new cost functions","volume":"339","author":"Jafarian","year":"2018","journal-title":"Appl. Math. Comput."},{"key":"ref_21","unstructured":"Beale, M.H., Hagan, M.T., and Demuth, H.B. (2010). Neural Network Toolbox. User\u2019s Guide, Version 7, The Mathworks, Inc.. Available online: https:\/\/www2.cs.siu.edu\/$\\sim$rahimi\/cs437\/slides\/nnet.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Nieto, A., Medrano, N., Sanz-Pascual, M.T., and Calvo, B. (2018, January 25\u201328). An accurate analysis method for complex IC analog neural network-based systems using high-level software tools. Proceedings of the IEEE 9th Latin American Symposium on Circuits & Systems (LASCAS), Puerto Vallarta, M\u00e9xico.","DOI":"10.1109\/LASCAS.2018.8399902"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1002\/cta.450","article-title":"Using MOS Current Dividers for Linearization of Programmable Gain Amplifiers","volume":"36","author":"Sanz","year":"2008","journal-title":"Int. J. Circuit Theory Appl."},{"key":"ref_24","unstructured":"Omondi, A.R. (1994). Computer Arithmetic Systems, Algorithms, Architecture and Implementations, Prentice Hall International."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1109\/72.471364","article-title":"The effects of quantization on multilayer neural networks","volume":"6","author":"Dundar","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/72.750571","article-title":"Worst case analysis of weight inaccuracy effects in multilayer perceptrons","volume":"10","author":"Anguita","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1016\/j.proeng.2016.11.491","article-title":"A CMOS Mixed Mode Non-Linear Processing Unit for Adaptive Sensor Conditioning in Portable Smart Systems","volume":"168","author":"Marquez","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Esparza-Alfaro, F., Lopez-Martin, A., Carvajal, R.G., and Ramirez-Angulo, J. (2014). Highly linear micropower class AB current mirrors using Quasi-Floating Gate transistors. Microelectron. J., 45.","DOI":"10.1016\/j.mejo.2014.02.006"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1049\/el:20082419","article-title":"Compact class AB CMOS current mirror","volume":"44","author":"Carvajal","year":"2008","journal-title":"Electron. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Carrasco-Robles, M., and Serrano, L. (2008, January 18\u201321). A novel CMOS current mode fully differential Tanh(x) implementation. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Seattle, WA, USA.","DOI":"10.1109\/ISCAS.2008.4541878"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Babu, V.S., and Baiju, M.R. (2008, January 7\u20139). Adaptive Neuron Activation Function with FGMOS Based Operational Transconductance Amplifier. Proceedings of the 2008 IEEE Computer Society Annual Symposium on VLSI, Montpellier, France.","DOI":"10.1109\/ISVLSI.2008.12"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1109\/JSEN.2008.920713","article-title":"Analog Neural Network Implementation for a Real-Time Surface Classification Application","volume":"8","author":"Gatet","year":"2008","journal-title":"IEEE Sens. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","article-title":"Sigmoid-weighted linear units for neural network function approximation in reinforcement learning","volume":"107","author":"Elfwing","year":"2018","journal-title":"Neural Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e1255","DOI":"10.1016\/j.na.2009.01.124","article-title":"Neural networks with sigmoidal activation functions\u2014Dimension reduction using normal random projection","volume":"71","year":"2009","journal-title":"Nonlinear Anal. Theory Methods Appl."},{"key":"ref_35","unstructured":"Cadence Design Systems (2001). Cadence Ocean Reference, Cadence Design Systems."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Nieto, J.A., Sanz-Pascual, M.T., and Medrano-Marqu\u00e9s, N.J. (2017, January 4\u20136). Integrated mixed mode neural network implementation. Proceedings of the European Conference on Circuit Theory and Design (ECCTD), Catania, Italy.","DOI":"10.1109\/ECCTD.2017.8093233"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Roja, R. (1996). The Backpropagation Algorithm, Chapter 7: Neural Networks, Springer.","DOI":"10.1007\/978-3-642-61068-4_7"},{"key":"ref_38","unstructured":"Smith, J.S., Wu, B., and Wilamowski, B.M. (2018). Neural Network Training with Levenberg-Marquardt and Adaptable Weight Compression. IEEE Trans. Neural Netw. Learn. Syst., 1\u20138."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Reynaldi, A., Lukas, S., and Margaretha, H. (2012, January 14\u201316). Backpropagation and Levenberg-Marquardt Algorithm for Training Finite Element Neural Network. Proceedings of the UKSim\/AMSS 6th European Symposium on Computer Modeling and Simulation, Valetta, Malta.","DOI":"10.1109\/EMS.2012.56"},{"key":"ref_40","unstructured":"Watson, G.A. (1978). The Levenberg-Marquardt algorithm: Implementation and theory. Numerical Analysis, Springer."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/S0890-6955(00)00073-0","article-title":"Training multilayered perceptrons for pattern recognition: A comparative study of four training algorithms","volume":"41","author":"Pham","year":"2001","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_42","unstructured":"Marinov, M., Dimirtov, S., Djamiykov, T., and Dontscheva, M. (2004, January 13\u201316). An adaptive approach for linearization of temperature sensor characteristics. Proceedings of the 27th International Spring Seminar on Electronics Technology: Meeting the Challenges of Electronics Technology Progress, Bankya, Bulgaria."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.ijleo.2017.11.034","article-title":"Temperature compensation readout integrated circuit for microbolometric focal plane array","volume":"155","author":"Chen","year":"2018","journal-title":"Optik"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/TIM.2008.2003320","article-title":"Linearization Circuit of the Thermistor Connection","volume":"58","author":"Nenova","year":"2009","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, C.-C., Chen, C.-L., and Lin, Y. (2016). All-Digital Time-Domain CMOS Smart Temperature Sensor with On-Chip Linearity Enhancement. Sensors, 16.","DOI":"10.3390\/s16020176"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.sna.2018.11.004","article-title":"Temperature compensation of NTC thermistors based anemometer","volume":"285","author":"Atanasijevic","year":"2018","journal-title":"Sens. Actuators A Phys."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/8\/1814\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:45:50Z","timestamp":1760186750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/8\/1814"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,16]]},"references-count":46,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["s19081814"],"URL":"https:\/\/doi.org\/10.3390\/s19081814","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,4,16]]}}}