با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Solder joint reliability risk estimation by AI modeling
برآورد خطر قابلیت اطمینان اتصال لحیم کاری با مدل سازی هوش مصنوعی -2020 This paper studies AI modeling for the solder joint
fatigue risk estimation under the thermal cycle loading of
redistributed wafer level packaging. The artificial neural
network (ANN), recurrent neural network (RNN) and
vectorized-gate network long short-term memory (VNLSTM)
architectures have been trained by the same dataset
to investigate their performance for this task. The learning
accuracy criterion, the implementation of all neural
network architecture, the learning results and result
analysis would be covered.
Because the involvement of the time/temperaturedependent
nonlinearity material characteristics, it is
recommended that more than three hidden layers and a
proper neural network architecture, which is capable of the
sequential data processing, should be considered in order
to guarantee the required accuracy and the satisfied
convergence speed. Keywords: Solder joint fatigue risk estimation | Time/temperature-dependent nonlinearity | ANN | RNN | LSTM | machine learning |
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