000097416 001__ 97416
000097416 005__ 20231108202300.0
000097416 0247_ $$2doi$$a10.1109/ACCESS.2020.3038052
000097416 0248_ $$2sideral$$a121794
000097416 037__ $$aART-2020-121794
000097416 041__ $$aeng
000097416 100__ $$aMartinez-Nieto, J.A.
000097416 245__ $$aMicroelectronic cmos implementation of a machine learning technique for sensor calibration
000097416 260__ $$c2020
000097416 5060_ $$aAccess copy available to the general public$$fUnrestricted
000097416 5203_ $$aAn 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.
000097416 536__ $$9info:eu-repo/grantAgreement/ES/MCIU/PID2019-106570RB-I00-AEI-10.13039-501100011033
000097416 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000097416 590__ $$a3.367$$b2020
000097416 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b65 / 161 = 0.404$$c2020$$dQ2$$eT2
000097416 591__ $$aTELECOMMUNICATIONS$$b36 / 91 = 0.396$$c2020$$dQ2$$eT2
000097416 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b94 / 273 = 0.344$$c2020$$dQ2$$eT2
000097416 592__ $$a0.586$$b2020
000097416 593__ $$aComputer Science (miscellaneous)$$c2020$$dQ1
000097416 593__ $$aMaterials Science (miscellaneous)$$c2020$$dQ1
000097416 593__ $$aEngineering (miscellaneous)$$c2020$$dQ1
000097416 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000097416 700__ $$aSanz-Pascual, M.T.
000097416 700__ $$0(orcid)0000-0002-5380-3013$$aMedrano, N.$$uUniversidad de Zaragoza
000097416 700__ $$0(orcid)0000-0003-2361-1077$$aCalvo Lopez, B.$$uUniversidad de Zaragoza
000097416 700__ $$0(orcid)0000-0003-4404-776X$$aAntolín Cañada, D.
000097416 7102_ $$15008$$2250$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Electrónica
000097416 773__ $$g8 (2020), 207367-207376$$pIEEE Access$$tIEEE Access$$x2169-3536
000097416 8564_ $$s1299765$$uhttps://zaguan.unizar.es/record/97416/files/texto_completo.pdf$$yVersión publicada
000097416 8564_ $$s512363$$uhttps://zaguan.unizar.es/record/97416/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000097416 909CO $$ooai:zaguan.unizar.es:97416$$particulos$$pdriver
000097416 951__ $$a2023-11-08-20:15:31
000097416 980__ $$aARTICLE