Optimization of the Soldering Process With ECAs in Electronic Equipment: Characterization Measurement and Experimental Design

Fort

Mugnaini

et al. 2017

Sensors

Self Cite

Eight different types of nanostructured perovskites based on YCoO3 with different chemical compositions are prepared as gas sensor materials, and they are studied with two target gases NO2 and CO. Moreover, a statistical approach is adopted to optimize their performance. The innovative contribution is carried out through a split-plot design planning and modeling, also involving random effects, for studying Metal Oxide Semiconductors (MOX) sensors in a robust design context. The statistical results prove the validity of the proposed approach; in fact, for each material type, the variation of the electrical resistance achieves a satisfactory optimized value conditional to the working temperature and by controlling for the gas concentration variability. Just to mention some results, the sensing material YCo0.9Pd0.1O3 (Mt1) achieved excellent solutions during the optimization procedure. In particular, Mt1 resulted in being useful and feasible for the detection of both gases, with optimal response equal to +10.23% and working temperature at 312∘C for CO (284 ppm, from design) and response equal to −14.17% at 185∘C for NO2 (16 ppm, from design). Analogously, for NO2 (16 ppm, from design), the material type YCo0.9O2.85+1%Pd (Mt8) allows for optimizing the response value at −15.39% with a working temperature at 181.0∘C, whereas for YCo0.95Pd0.05O3 (Mt3), the best response value is achieved at −15.40% with the temperature equal to 204∘C.

Section: Preparation Of the Sensing Materials Sensor Realization mentioning

confidence: 99%

Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design

Fort

Mugnaini

et al. 2017

Sensors

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“…By this way the costs of chemical sensors characterization in terms of man-hours and budget are minimized; quantitative results and learning [21], [22] are maximized.…”

Section: Introductionmentioning

confidence: 99%

Assessment and Optimization for Novel Gas Materials Through the Evaluation of Mixed Response Surface Models

IEEE Trans. Instrum. Meas.

Fort

Vignoli

et al. 2015

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In this paper, an innovative methodology aimed at improving the development of novel gas sensors through a process optimization is carried out by applying mixed response surface (RS) models. High accuracy measurements of new conductometric metal oxide gas sensors, obtained by an efficient control of the working conditions, are gathered. The response of metal–oxide–semiconductor gas sensors changes significantly when the sensors operate at different temperatures and target gas concentrations. To consider all the sources of variability there involved, the RS methodology was applied, including random effects, to improve and optimize the performance of these new gas sensors. More precisely, the optimization is performed exploiting a limited number of observations, systematically collected with an ad hoc measurement system, and it considers external sources of variability, satisfying at the same time stringent requirements. Furthermore, the statistical results and the relative assessment of novel gas materials are obtained by considering fixed as well as random effects, where random variables are considered for\ud better controlling the optimization step

“…It must be noted that there are two reasons for choosing to evaluate the two adhesives through a block factor: (i) the two adhesives previously described are different in microstructure but, at the same time, both of them could represent an alternative to lead-free solders with a general validity; thus, this technical difference may be evaluated through a block factor and (ii) the study is closely related to the two analyzed adhesives simply because their technical structure could be valid for replacing lead solders in microelectronic devices. Further details related to specimen production techniques and conduction of trials are reported in [14].…”

Section: Specimen Production and Experimental Designmentioning

confidence: 99%

Mixed response surface models and Bayesian analysis of variance components for electrically conductive adhesives

Berni

Scarano

Appl Stoch Models Bus & Ind

et al. 2013

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This paper deals with an analysis of random effects for microelectronic data. More precisely, by considering the technical challenges related to the use of electrically conductive adhesives such as soldering material in electronics, the sources of variabilities related to different electrically conductive adhesive characteristics and working process variables are evaluated. Random effects are involved in a response surface methodology setting, and the results are compared with a Bayesian approach where variance components are estimated through a log-posterior expressed as the product of the information matrix and restricted maximum likelihood estimates of variance components.