Measurement of in-plane thermal diffusivity of thin film using a regression methodology based on Bayesian...

Area of Science:

• Materials Science
• Thermal Physics
• Nanotechnology

Background:

• Lock-in thermography is a standard method for determining thermal properties of films.
• High phase detection accuracy in lock-in thermography often necessitates advanced infrared (IR) cameras.
• Conventional methods treat amplitude and phase as intermediate values, requiring complex algorithms.

Purpose of the Study:

• To develop a regression methodology using Bayesian optimization for determining in-plane thermal diffusivity of thin films.
• To present an alternative to conventional lock-in algorithms that directly uses time-sequential thermograms.
• To reduce system requirements for accurate thermal property characterization.

Main Methods:

• A regression methodology based on Bayesian optimization is proposed.
• The method directly incorporates time-sequential thermograms into the regression process, bypassing intermediate amplitude and phase calculations.
• Thermal diffusivity and phase offset are extracted by minimizing the mean absolute error between measured and simulated normalized temperatures.

Main Results:

• Accurate in-plane thermal diffusivity was determined using only a few thermograms per modulation period.
• The method was validated on a stainless-steel film, showing robust performance.
• The approach demonstrated reduced system requirements compared to traditional methods.

Conclusions:

• The proposed Bayesian optimization method offers a feasible and robust alternative for determining thin film thermal diffusivity.
• This approach simplifies the experimental setup by not requiring sophisticated IR cameras for high phase accuracy.
• The technique shows promise for characterizing complex materials like composite films and advanced thermal interfacial materials.