In-situ investigation of EMC relaxation behavior using piezoresistive stress sensor

Palczynska, Alicja; Gromala, Przemyslaw Jakub; Mayer, Dirk; Han, Bongtae; Melz, Tobias

doi:10.1016/j.microrel.2016.03.013

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…The viscoelastic effect and stress relaxation behavior are obtained by utilizing dynamic mechanical analysis [89] and sensor-aided measurement [43,90]. With the carefully measured material parameters, FEM models are constructed and verified.…”

Section: Improvement In Thermal-performance Stabilitymentioning

confidence: 99%

“…Firstly, the resistance of piezoresistors and piezoresistive coefficient will vary with the temperature, which will influence the zero-offset and sensitivity of the device [38]. Then, as a stress-based transduction device, the residual stresses from the chip fabrication process [39,40], wafer bonding [41], adhesion and compound molding [42,43] can lead to obvious variations in sensor parameters. Meantime, the difference of coefficient of thermal expansion (CTE) between the materials will induce a significant accompanying stress in the device, and the effect varies under different ambient temperatures for different sensors [44,45,46].…”

Section: Introductionmentioning

confidence: 99%

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Thermal-Performance Instability in Piezoresistive Sensors: Inducement and Improvement

Liu

Wang

Zhao

et al. 2016

Sensors

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The field of piezoresistive sensors has been undergoing a significant revolution in terms of design methodology, material technology and micromachining process. However, the temperature dependence of sensor characteristics remains a hurdle to cross. This review focuses on the issues in thermal-performance instability of piezoresistive sensors. Based on the operation fundamental, inducements to the instability are investigated in detail and correspondingly available ameliorative methods are presented. Pros and cons of each improvement approach are also summarized. Though several schemes have been proposed and put into reality with favorable achievements, the schemes featuring simple implementation and excellent compatibility with existing techniques are still emergently demanded to construct a piezoresistive sensor with excellent comprehensive performance.

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Section: Improvement In Thermal-performance Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Thermal-Performance Instability in Piezoresistive Sensors: Inducement and Improvement

Liu

Wang

Zhao

et al. 2016

Sensors

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show abstract

“…It is known that the process of curing always accompanies cure shrinkage (Schindler-Saefkow et al , 2014). That is, the visco-elastic behavior of the thermosetting EMC is affected by both operation temperature and time (Palczynska et al , 2015; Gromaia et al , 2015) and could have a large impact on package reliability (Shen et al , 2014). The shrinkage-related internal residual stress is especially large at higher temperatures.…”

Section: Resultsmentioning

confidence: 99%

Process factors affecting adhesion of encapsulation molding compounds

Liaw

Chao

Chou

2018

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Purpose The purpose of this paper is to determine the key process factors which affect the adhesion strength of encapsulation molding compounds (EMCs) to leadframes to obtain reliable components without any need to pretreat the leadframe surface. Design/methodology/approach EMCs were molded to Cu leadframes to experimentally quantify the effect of mold temperature, resin viscosity, leadframe oxidation and powder moisture on the adhesion force. Component reliability was assessed by PCT. Findings A higher mold temperature result in a larger adhesion force. The mold temperature of 175°C provides the largest process window. Leadframe oxidation can increase adhesion first, but then decrease it drastically with further oxidation. The powder moisture content has mixed effect on adhesion. Practical implications By molding at 175°C, limiting the wire bonding time and minimizing the powder moisture content, reliable components can be obtained without any need for leadframe surface pretreatment such as plasma cleaning or surface coating. Originality/value Quantify the key process factors which affect the adhesion of EMCs and reveal reason behind the current industrial practice of using the mold temperature of 175°C.

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“…One of the ways to perform prognostics is to measure the mechanical stresses directly using a stress sensor. Such sensor was developed originally to measure the manufacturing stresses [4], but was extended successfully to various applications including transfer molding [5], packaging [6], molding relaxation [7], prognostics [8] [9] and condition monitoring [10] [11]. In [12] the sensor was packages in a QFN and the stress field has been recorded after the damage.…”

Section: Introductionmentioning

confidence: 99%

Degradation Estimation and Prediction of Electronic Packages Using Data-Driven Approach

Prisacaru

Gromala

Han

et al. 2022

IEEE Trans. Ind. Electron.

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Recent trends in automotive electronics such as automated driving will increase the number and complexity of electronics used in safety relevant applications. Applications in logistics or ridesharing will require a specific year of service rather than the conventional mileage usage. Reliable operations of the electronic systems must be assured at all times, regardless of the usage condition. A more dynamic and on-demand way of assuring the system availability will have to be developed. This paper proposes a thermo-mechanical stress-based prognostics method as a potential solution. The goal is achieved by several novel advancements. On the experimental front, a key microelectronics package is developed to directly apply the prognostics and health management (PHM) concept using a piezoresistive silicon-based stress sensor. Additional hardware for safe and secure data transmission and data-processing is also developed, which is critically required for recording in situ and real-time data. On the data-management front, proper data-driven approaches have to be identified to handle the unique data set from the stress sensor employed in the study. The approaches effectively handle the massive amount of data that reveals the important information and automation of the prognostic process and thus to be able to detect, classify, locate and predict the failure. The statistical techniques for diagnostics and the machine learning (ML) algorithms for health assessment and prognostics are also determined to implement the approaches in a simple, fast but accurate way within the capacity of limited computing power. The proposed prognostics approach is implemented with actual microelectronics packages subjected to harsh accelerated testing conditions. The results corroborate the validity of the proposed prognostics approach.

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In-situ investigation of EMC relaxation behavior using piezoresistive stress sensor

Cited by 9 publications

References 6 publications

Thermal-Performance Instability in Piezoresistive Sensors: Inducement and Improvement

Thermal-Performance Instability in Piezoresistive Sensors: Inducement and Improvement

Process factors affecting adhesion of encapsulation molding compounds

Degradation Estimation and Prediction of Electronic Packages Using Data-Driven Approach

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