System and method for testing thermal conductivity of thin film materials based on machine learning

Abstract

The invention discloses a system and method for testing thermal conductivity of thin film materials based on machine learning, and the system comprises a pressure loading end, a deformation testing end, a surface scanning end, a material scanning end, an external environment simulation end, an infrared temperature detection end, a could computing learning end, a data output end and a result feedback correction end. The method provided by the invention firstly preprocesses submicron or nanometer thin film materials by means of pressurization, thickness measurement, surface topography and element composition analysis, simulation of temperature and humidity conditions of the application environment, and accurate infrared temperature measurement to obtain basic condition parameters, receives data through the host of the cloud computing learning end, builds a model by using a method of statistical machine learning to calculate and predict the thermal conductivity coefficients and interfacethermal resistance thereof, monitors the cloud computing learning end in real time, and constantly modifies the prediction model and algorithm to finally obtain the optimal submicron or nano thermal conductivity coefficient and interface thermal resistance prediction results.

Description

technical field [0001] The invention relates to the field of steady-state testing of thermal physical properties of materials, in particular to a machine learning-based testing system and method for thermal conductivity of thin film materials. Background technique [0002] Machine learning is the science of how to use computers to simulate or realize human learning activities. It is one of the most intelligent and cutting-edge research fields in artificial intelligence. Since the 1980s, machine learning, as a way to realize artificial intelligence, has aroused widespread interest in the field of artificial intelligence. of the subject. Machine learning has been applied not only in knowledge-based systems, but also in many fields such as natural language understanding, non-monotonic reasoning, machine vision, pattern recognition, etc. Whether a system has the ability to learn has become a sign of "intelligence". The research of machine learning is mainly divided into two t...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the problems of time-consuming and labor-intensive testing methods, large testing errors and inaccurate results

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