97416 20231108202300.0 doi 10.1109/ACCESS.2020.3038052 sideral 121794 ART-2020-121794 eng Martinez-Nieto, J.A. Microelectronic cmos implementation of a machine learning technique for sensor calibration 2020 Access copy available to the general public Unrestricted An integrated machine-learning based adaptive circuit for sensor calibration implemented in standard 0.18μm CMOS technology with 1.8V power supply is presented in this paper. In addition to linearizing the device response, the proposed system is also capable to correct offset and gain errors. The building blocks conforming the adaptive system are designed and experimentally characterized to generate numerical high-level models which are used to verify the proper performance of each analog block within a defined multilayer perceptron architecture. The network weights, obtained from the learning phase, are stored in a microcontroller EEPROM memory, and then loaded into each of the registers of the proposed integrated prototype. In order to verify the proposed system performance, the non-linear characteristic of a thermistor is compensated as an application example, achieving a relative error er below 3% within an input span of 130°C, which is almost 6 times less than the uncorrected response. The power consumption of the whole system is 1.4mW and it has an active area of 0.86mm 2 . The digital programmability of the network weights provides flexibility when a sensor change is required. info:eu-repo/grantAgreement/ES/MCIU/PID2019-106570RB-I00-AEI-10.13039-501100011033 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 3.367 2020 COMPUTER SCIENCE, INFORMATION SYSTEMS 65 / 161 = 0.404 2020 Q2 T2 TELECOMMUNICATIONS 36 / 91 = 0.396 2020 Q2 T2 ENGINEERING, ELECTRICAL & ELECTRONIC 94 / 273 = 0.344 2020 Q2 T2 0.586 2020 Computer Science (miscellaneous) 2020 Q1 Materials Science (miscellaneous) 2020 Q1 Engineering (miscellaneous) 2020 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sanz-Pascual, M.T. Medrano, N. Universidad de Zaragoza (orcid)0000-0002-5380-3013 Calvo Lopez, B. Universidad de Zaragoza (orcid)0000-0003-2361-1077 Antolín Cañada, D. (orcid)0000-0003-4404-776X 5008 250 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Electrónica 8 (2020), 207367-207376 IEEE Access IEEE Access 2169-3536 1299765 http://zaguan.unizar.es/record/97416/files/texto_completo.pdf Versión publicada 512363 http://zaguan.unizar.es/record/97416/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:97416 articulos driver 2023-11-08-20:15:31 ARTICLE