Cycle life and statistical predictive reliability model for all-solid-state thin film microbatteries. (February 2019)

• Record Type: Journal Article
• Authors: Grillon, Nathanaël; Bouyssou, Émilien; Jacques, Sébastien; Gautier, Gaël
• Is Part Of: Microelectronics and reliability. Volume 93(2019)
• Page Start: 102
• Page End: 108
• Publication Date: 2019-02
• Subjects: Microbattery -- Cycle life -- Aging modeling -- Reliability; Electronic apparatus and appliances -- Reliability -- Periodicals; Miniature electronic equipment -- Periodicals; Appareils électroniques -- Fiabilité -- Périodiques; Équipement électronique miniaturisé -- Périodiques; Electronic apparatus and appliances -- Reliability; Miniature electronic equipment; Periodicals; 621.3815
• DOI: 10.1016/j.microrel.2019.01.003
• Languages: English
• ISSNs: 0026-2714

Abstract

Abstract: This paper focuses on the development of a predictive cycle life model of all-solid-state thin film Cu/Li/LiPON/LiCoO2 microbatteries based on experimental accelerated aging tests. More than 120 samples were tested in 8 different conditions including several electrochemical cycling modes and different temperature levels. Hence, an original reliability model, based on an exponential function especially designed to describe the degradation mechanism occurring at the positive electrode, is proposed to describe the observed capacity loss evolution of microbatteries. An extended mathematical model, which includes a large set of accelerated factors, physicochemical considerations and statistical parameters is then built around such an exponential function. This model thus enables to simulate a wide variety of battery aging profiles from input parameters such as temperature, depth of charge ( DoC ), discharge rate, failure rate and capacity fade. We believe that the development of such a reliability model is of most importance not only from an industrial point of view, since it enables to determine precisely the expected behavior of microbatteries in various mission profile conditions, but it represents also a key tool that enables to further understand the observed degradation mechanisms, and to identify any technological optimization opportunities. Highlights: Evaluation of intrinsic reliability performances of solid-state microbatteries Introduction of an exponential function to fit aging data from a reliability study The predictive model enables to simulate a wide variety of battery aging profiles. Possibility to consider a statistical dimension