Design and Characteristics of a Thin-Film ThermoMesh for the Efficient Embedded Sensing of a Spatio-Temporally Sparse Heat Source
2026-04-30 • Robotics
Robotics
AI summaryⓘ
The authors developed ThermoMesh, a thin mesh sensor that detects small, spread-out heat sources by conduction rather than infrared imaging. It uses special layers that change resistance with temperature to sense heat and compress data at the same time. Their models show that nonlinear materials like ceramic NTC or VO₂ layers greatly improve sensitivity and accuracy, especially for larger sensors. Testing with simulated noisy data showed high accuracy in locating and measuring heat. This suggests ThermoMesh could be useful for thermal sensing in tough environments where usual infrared cameras don't work well.
thermoelectric junctionthermal imagingresistive interlayernegative-temperature-coefficient (NTC)metal–insulator transition (MIT)signal-to-noise rationoise-equivalent temperature (NET)spatio-temporal sparsitythin-film sensor
Authors
Sajjad Boorghan Farahan, Ahmed Alajlouni, Jingzhou Zhao
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--1273~K 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--373~K yields a $\sim24\times$ improvement. Using synthetic 1-sparse datasets with white boundary-channel noise at a signal-to-noise ratio of 40~dB, 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.