Design and Characteristics of a Thin-Film ThermoMesh for the Efficient Embedded Sensing of a Spatio-Temporally Sparse Heat Source

TL;DR

ThermoMesh is a passive thin-film thermoelectric sensor array designed for efficient detection and characterization of sparse heat sources, with enhanced sensitivity through nonlinear interlayers and promising accuracy in thermal imaging.

Contribution

This work introduces ThermoMesh, a novel thermoelectric mesh sensor with nonlinear interlayers that significantly improve sensitivity and localization accuracy for sparse heat sources.

Findings

Linear resistive interlayers improve minimum sensitivity tenfold.

Nonlinear interlayers like ceramic NTC and VO₂ greatly enhance sensitivity.

Synthetic data tests show high localization accuracy and low temperature error.

Abstract

This work presents ThermoMesh, a passive thin-film thermoelectric mesh sensor designed to detect and characterize spatio-temporally sparse heat sources through conduction-based thermal imaging. The device integrates thermoelectric junctions with linear or nonlinear interlayer resistive elements to perform simultaneous sensing and in-sensor compression. We focus on the single-event (1-sparse) operation and define four performance metrics: range, efficiency, sensitivity, and accuracy. Numerical modeling shows that a linear resistive interlayer flattens the sensitivity distribution and improves minimum sensitivity by approximately tenfold for a $16\times16$ mesh. Nonlinear temperature-dependent interlayers further enhance minimum sensitivity at scale: a ceramic negative-temperature-coefficient (NTC) layer over 973-1273K yields a $\sim14{,}500\times$ higher minimum sensitivity than the linear design at a $200\times200$ mesh, while a VO$_2$ interlayer modeled across its metal-insulator transition (MIT) over 298-373K yields a $\sim24\times$ improvement. Using synthetic 1-sparse datasets with white boundary-channel noise at a signal-to-noise ratio of 40dB, the VO$_2$ case achieved $98\%$ localization accuracy, a mean absolute temperature error of $0.23$K, and a noise-equivalent temperature (NET) of $0.07$K. For the ceramic-NTC case no localization errors were observed under the tested conditions, with a mean absolute temperature error of $1.83$K and a NET of $1.49$K. These results indicate that ThermoMesh could enable energy-efficient embedded thermal sensing in scenarios where conventional infrared imaging is limited, such as molten-droplet detection or hot-spot monitoring in harsh environments.