Machine learning-based analyses for total ionizing dose effects in bipolar junction transistors

Wang, Bai-Chuan; Qiu, Mengtong; Chen, Wei; Wang, Chen-Hui; Tang, Chuanxiang

doi:10.1007/s41365-022-01107-w

Cited by 9 publications

(2 citation statements)

References 47 publications

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“…The remaining 16 samples were stored in a separate input file and used to evaluate the model’s generalization ability, aiming to reduce potential correlations between the training input data and the generalization testing data. During the ANN training process, if the prediction accuracy fell below 85%, the ANN optimizer would be triggered to improve the structure of the ANN, including the number of hidden layers and neurons, and the activation functions (Wang et al , 2022). Among the over 200 ANN models generated by the optimizer, obtaining a high-accuracy network model was relatively straightforward.…”

Section: Neural Network Results and Discussionmentioning

confidence: 99%

Temperature and current density prediction in solder joints using artificial neural network method

Liu,

Xu,

et al. 2023

SSMT

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Purpose Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints. Design/methodology/approach The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data. Findings Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%. Originality/value The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

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Section: Neural Network Results and Discussionmentioning

confidence: 99%

Temperature and current density prediction in solder joints using artificial neural network method

Liu,

Xu,

et al. 2023

SSMT

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show abstract

“…Terefore, in the gate oxide layer, the radiation-induced oxide charge Q ox is directly proportional to the thickness of the oxide layer t ox , as N TH � −Q ox t ox /ε ox , and ε ox � C ox /t ox . As a result, the drift of the threshold voltage ∆V TH is proportional to t ox , and the relationship between the drift of the threshold voltage ∆V TH and the radiation dose D can be expressed as follows [19]:…”

Section: X-raymentioning

confidence: 99%

Overview on Radiation Damage Effects and Protection Techniques in Microelectronic Devices

Ren,

Zhu,

et al. 2024

Science and Technology of Nuclear Installations

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With the rapid advancement of information technology, microelectronic devices have found widespread applications in critical sectors such as nuclear power plants, aerospace equipment, and satellites. However, these devices are frequently exposed to diverse radiation environments, presenting significant challenges in mitigating radiation-induced damage. Hence, this review aims to delve into the intricate damage mechanisms of microelectronic devices within various radiation environments and highlight the latest advancements in radiation-hardening techniques. The ultimate goal is to bolster the reliability and stability of these devices under extreme conditions. The review initiates by outlining the spectrum of radiation environments that microelectronic devices may confront, encompassing space radiation, nuclear explosion radiation, laboratory radiation, and process radiation. It also delineates the potential damage types that these environments can inflict upon microelectronic devices. Furthermore, the review elaborates on the underlying mechanisms through which different radiation environments impact the performance of microelectronic devices, which includes a detailed analysis of the characteristics and fundamental mechanisms of damage when microelectronic devices are subjected to total ionizing dose effects and single-event effects. In addition, the review delves into the promising application prospects of several key radiation-hardening techniques for enhancing the radiation tolerance of microelectronic devices.

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Verification of neutron-induced fission product yields evaluated by a tensor decompsition model in transport-burnup simulations

et al. 2023

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Machine learning-based analyses for total ionizing dose effects in bipolar junction transistors

Cited by 9 publications

References 47 publications

Temperature and current density prediction in solder joints using artificial neural network method

Temperature and current density prediction in solder joints using artificial neural network method

Overview on Radiation Damage Effects and Protection Techniques in Microelectronic Devices

Verification of neutron-induced fission product yields evaluated by a tensor decompsition model in transport-burnup simulations

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