Thin-Film Batteries Failure Prediction from a Short Pulsed Test Using Machine Learning
Authors: Secouard Christophe , Chevalier Isabelle , Geffraye Françoise , Reyes Romain , Lamy Yann , Oukassi Sami 
Published: July 2022

We experimentally demonstrated for the first time in 2019 LiCoO2 all solid-state thin-film batteries with high electrochemical density storage of 0.89 mA.h.cm-2 [1]. Despite these excellent performances, some devices may be defective and fail during cycling. Therefore, in the context of industrial production of such devices, there is a need to reliably assess the functionality of the cells on the wafer scale. The most straightforward approach consists in simply cycling the batteries as well as performing complementary short tests such as Open-Circuit Voltage measurement (OCV) and Electrochemical Impedance Spectroscopy (EIS). However, with 14 000+ cells per wafer said approach is too time consuming. Moreover tested batteries would not be suitable for further commercialization as the electrodes have already been solicited, which could affect their shelf life.

We therefore have investigated on the ability of a short – i.e. with a duration in the order of the minute – pulsed test to predict the failure of our dies, using machine learning. This Electrical Wafer Sorting (EWS) or screening protocol consists in inducing a partial charge for 1 min and then to observe the relaxation by measuring the OCV for 10 s. OCV and EIS are also measured before and after the pulse. Certain features are extracted from profiles collected during the EWS protocol (shown in Figure a)), such as the mean OCV or the real part of the impedance at 1 kHz. 1122 devices were screened using this protocol and then cycled for 1 cycle (1 full charge 1 full discharge). The batteries were then classified in binary categories, according to the outcome of the cycling test: fail or pass. Fail category encompasses all failure modes: (e.g. open-circuit, short-circuit). The features extracted during the EWS were used to build the prediction model, whose target is the cycling categorization. Foremost, the data was split in a training set and a test set. In a first step, variable selection was achieved using a simple decision tree model. It enabled us to choose the combination of parameters leading to a maximization of the f1-scores (from 68 to 73 %). However, the learning curves revealed that this simple model clearly overfitted and was unable to generalize. In a second step, a variety of models were trained using the selected features, including Random Forest, Support Vector Machines, and Logistic Regression. The three models leading to best f1-scores were kept and their hyperparameters have been further optimized following a grid search approach. The algorithm giving the highest scores is the Random Forest classifier. Although it was not possible to increase its accuracy (still at 73%) the optimization step helped to significantly reduce the overfitting, reinforcing the ability of the model to be equally accurate on new data. The corresponding confusion matrix and learning curves are displayed on Figure b). Figure c) shows respectively predicted and actual wafermaps for data present in the dataset used to build the model. An example of predicted wafermap on a complete wafer is presented on Figure d).

Achieved performance provide an interesting base for investigations in a research context. It is shown that the functionality of the thin film batteries can be predicted with reasonable confidence. Nevertheless, far better scores (above 90%) are required for production application. Thus further work will focus on collecting new data to check on generalizability and if it helps to improve the model accuracy when used for training. All the work presented in this abstract has been carried out using scikit-learn Python package [2].

[1] S. Oukassi et al., “Millimeter scale thin film batteries for integrated high energy density storage,” 2019, vol. 2019-December. doi: 10.1109/IEDM19573.2019.8993483.

[2] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, no. 85, pp. 2825–2830, 2011.

Figure 1