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).
دسته بندی:
هوش مصنوعی - Artificial intelligence
سال انتشار:
2020
عنوان انگلیسی مقاله:
Solder joint reliability risk estimation by AI modeling
ترجمه فارسی عنوان مقاله:
برآورد خطر قابلیت اطمینان اتصال لحیم کاری با مدل سازی هوش مصنوعی
منبع:
IEEE - 2020 21st International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE);2020; ; ;
نویسنده:
Cadmus Yuan and Chang-Chi Lee
چکیده انگلیسی:
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
قیمت: رایگان
توضیحات اضافی:
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