A machine learning-based testing system and method for thermal conductivity of thin film materials

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 properties of materials, in particular to a system and method for testing thermal conductivity of thin film materials based on machine learning. Background technique [0002] Machine learning is the science of studying 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 artificial intelligence community. Especially in the past ten years, the research work in the field of machine learning has developed rapidly, and it has become an important part of artificial intelligence. Subject of it. Machine learning is not only used in knowledge-based systems, but also widely used in many fields such as natural language understanding, non-monotonic reasoning, machine visio...

AI Technical Summary

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

Images