Neural networks for enhanced stress prognostics for encapsulated electronic packages - A comparison
Introduction
With the advent of artificial intelligence (AI), learning algorithms are becoming progressively more efficient in failure root-cause analysis and subsequently at anticipating the likelihood of failures and at prognostics and health management (PHM) by collecting reams of performance data for microsystems and microelectronic packages. So AI has the potential to handle lifetime prediction based on big data analysis and thus contribute substantially to certain reliability challenges, as for example future safety paradigms for autonomous driving. In this paper, deep learning (DL) methods have been utilized as pivotal tools to identify the complex relationship between independent and interdependent variables and features, as it focuses on forecasting high-resolution stress distributions through time-series data on industry-grade samples evaluated during stress testing. This work is meant to contribute to establish data driven neural network models to be compared and eventually merged with physics-of-failure (PoF) based finite element models. Data driven models do not focus on a specific local failure parameter but rather use one or more potential failure indicators by condition monitoring to predict a critical situation. This work addresses the first paradigm, whereas a physics-of-failure approach and detailed stress-versus-delamination correlations will be sought after in another paper.
Currently, there is a paucity of research into functional data analysis algorithms in order to develop reliable and sophisticated predictive models for reliability assessments of electronic packages, which may also be caused by a lack of meaningful data. However, in long term, the AI-based approach may facilitate the lifetime approximation of electronic packages. At present, the purpose of this study is the use of different deep neural network (DNN) models to create and benchmark a robust prediction, which could be used for reliability assessments in heterogeneously integrated microelectronic packages [1].
A common failure mode observed in electronic packages is the delamination at different sections of the package, which starts with a crack formation and continues growing at the interface of two dissimilar materials (cf. Fig. 1). The most common cause for such behavior is the internal stress existing at the interfaces, usually caused by the difference in the coefficient of thermal expansion and/or moisture preceded by interfacial defects, see e.g. [2,3] for reference. The physics-of-failure based lifetime prediction of interface delamination by fracture mechanical methods is subject of current R&D work [[4], [5], [6]], which represents the flipside of AI-based methods to be merged into hybrid models for future functional safety concepts [7].
In a previous study [8], test vehicles (TV) were used to create a prognostic model and neural networks (NN) were trained to predict the degradation of the packages. The trained NNs were capable to retrieve information about the structural changes in the packages, including delamination. The final delamination is monitored using scanning acoustic microscopy (SAM) as shown in Fig. 2.
Section snippets
Introduction on artificial intelligence, machine learning, and deep learning
Artificial intelligence (AI) technology matured over the last few years into a disruptive technology. Breakthroughs in a technique called deep learning set new performance levels for various AI applications. The currently evolving fields include predictive maintenance [9,10], quality control [11], smart automation [12] and process optimization [13].
Quite often, the terms machine learning (ML) and AI have been used interchangeably, which is not true. ML is a part that helps the AI to learn and
Experimental background
In order to improve the PHM in electronic packages, one option is to acquire useful data relevant to their reliability, called failure indicators. In this research, mechanical stress distributions were used to gather this type of data. Throughout the data acquisition process, stress measurements were collected during experiments from sensors (cf. Fig. 4, Fig. 5), embedded in test vehicles (TVs). The data was recorded in a structured way.
The TVs, the setup and the method of data collection is
Overview on neural networks
In this study, six prognostic configurations were trained to predict the stress distributions based on time-series data. In all cases, the training is empowered by backpropagation of error [22] and therefore all used algorithms originate from the class of supervised learning algorithms. During the training phase, input-output pairs are used to compute the loss function, describing the overall error of the prediction, and the gradient of the loss with respect to the given weights w used by the
Stress prediction using neural networks: results, discussion and comparison
During the training phase, all six networks have been trained using the same scheme. Following a 95% vs. 5% split, the available data (cf. Section 3, 4450 and 4444 measurements points, respectively) for each chip was divided into a training set and a much smaller validation set. The networks are trained multiple times, given by the number of epochs, on the training data. In the here presented case, the number of epochs is given by 50.
Conclusions and outlook
Results of the current study have shown, that gated-RNN models such as LSTM and GRU networks can provide promising prognostic results on stress distribution inside of encapsulated standard packages in different use cases. The simpler networks studied in the paper are generally quicker trained than the gated-RNN models but are unable to successfully fulfill the given tasks.
In this research, it was shown that certain neural networks can accurately predict the behavior of a single chip; however,
CRediT authorship contribution statement
Peter Meszmer: Conceptualization, Methodology, Software, Validation, Writing, Visualization, Supervision, Project administration
Mehryar Majd: Methodology, Software, Formal analysis, Investigation, Data curation, Visualization
Alexandru Prisacaru: Investigation, Resources
Przemyslaw Jakub Gromala: Validation, Supervision
Bernhard Wunderle: Conceptualization, Validation, Supervision.
Declaration of competing interest
None.
References (41)
Experimental determination of critical adhesion energies with the advanced button shear test
Microelectron. Reliab.
(2019)Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data
Procedia CIRP
(2019)A predictive model for the maintenance of industrial machinery in the context of industry 4.0
Eng. Appl. Artif. Intell.
(2020)- et al.
Intelligent laser welding through representation, prediction, and control learning: an architecture with deep neural networks and reinforcement learning
Mechatronics
(2016) Optimisation of manufacturing process parameters using deep neural networks as surrogate models
Procedia CIRP
(2018)Deep learning in neural networks: an overview
Neural Netw.
(2015)Towards prognostics and health monitoring: the potential of fault detection by piezoresistive silicon stress sensor
Microelectron. Reliab.
(2017)- et al.
A learning rule for very simple universal approximators consisting of a single layer of perceptrons
Neural Netw.
(2008) Neocognitron: a hierarchical neural network capable of visual pattern recognition
Neural Netw.
(1988)- et al.
Subject independent facial expression recognition with robust face detection using a convolutional neural network
Neural Netw.
(2003)
Prognostics and health monitoring of electronic system: a review
Thermo-mechanical reliability during technology development of power chip-on-board assemblies with encapsulation
Microsyst. Technol.
Lifetime modeling for microsystems integration - from nano to systems
J. Microsyst. Technol.
An in-situ numerical-experimental approach for fatigue delamination characterization in microelectronic packages
Degradation prediction of electronic packages using machine learning
Machine learning techniques for quality control in high conformance manufacturing environment
Adv. Mech. Eng.
Introduction to Machine Learning With Python and Scikit-Learn
Scikit-learn
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Former student.