Prediction of thermal conductivity of diamond film by neural network based on first principles

Wang, Biao; Ren, Haijie; Cao, Waixiang; Hu, Yanwei; He, Yurong; Han, Jiecai; Zhu, Jiaqi

doi:10.1360/sspma-2022-0036

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…On the other hand, by predicting the lifespan of electronic components, more reasonable maintenance plans can be formulated. For example, a more suitable maintenance cycle and replacement time for components can be determined based on the predicted results to reduce unnecessary maintenance work and costs [5]. In response to this, reference [6] proposed a method for predicting the lifespan of electronic components based on model fusion.…”

Section: Introductionmentioning

confidence: 99%

A life prediction method for electronic components based on dual-channel multimodal convolutional networks

Gu,

Zhang,

Yong

et al. 2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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Due to the multiple factors that affect the lifespan of electronic components, it is difficult to accurately predict the lifespan of electronic components by directly analyzing a single factor. Therefore, a study on the lifespan prediction method of electronic components based on dual channel multimodal convolutional networks is proposed. Based on the failure mechanism of electronic components, degradation models of electronic component electrical characteristic parameters were constructed from the perspectives of resistance and capacitance. Two parallel convolutional channels are used to extract continuous samples with uniform sampling time intervals. The degradation characteristics of electronic component electrical characteristic parameters at different time points extracted by the two parallel convolutional channels are fused, so that the final extracted electronic component features have temporal information. Multimodal fitting is performed on the electronic component features at the output of the convolutional network to obtain the final life prediction result. In the test results, the prediction error of the design method is stable within 6.0%, the time cost is stable within 21.0s, and fast convergence can be achieved.

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Section: Introductionmentioning

confidence: 99%