This paper proposes a real-time thermal monitoring method using embedded integrated sensor interfaces dedicated to industrial integrated system applications. Industrial sensor interfaces are complex systems that involve analog and mixed signals, where several parameters can influence their performance. These include the presence of heat sources near sensitive integrated circuits, and various heat transfer phenomena need to be considered. This creates a need for real-time thermal monitoring and management. Indeed, the control of transient temperature gradients or temperature differential variations as well as the prediction of possible induced thermal shocks and stress at early design phases of advanced integrated circuits and systems are essential. This paper addresses the growing requirements of microelectronics applications in several areas that experience fast variations in high-power density and thermal gradient differences caused by the implementation of different systems on the same chip, such as the new-generation 5G circuits. To mitigate adverse thermal effects, a real-time prediction algorithm is proposed and validated using the MCUXpresso tool applied to a Freescale embedded sensor board to monitor and predict its temperature profile in real time by programming the embedded sensor into the FRDM-KL26Z board. Based on discrete temperature measurements, the embedded system is used to predict, in advance, overheating situations in the embedded integrated circuit (IC). These results confirm the peak detection capability of the proposed algorithm that satisfactorily predicts thermal peaks in the FRDM-KL26Z board as modeled with a finite element thermal analysis tool (the Numerical Integrated elements for System Analysis (NISA) tool), to gauge the level of local thermomechanical stresses that may be induced. In this paper, the FPGA implementation and comparison measurements are also presented. This work provides a solution to the thermal stresses and local system overheating that have been a major concern for integrated sensor interface designers when designing integrated circuits in various high-performance technologies or harsh environments.

In this study, we compared the performance of quantum temperature sensors (QTSs) with conventional sensors (CSs), highlighting differences in measurement accuracy and stability. Quantum sensors (QSs), known for their ability to provide ultra-precise measurements (UPMs), were tested across a temperature range of −10 to 40 °C. The results indicate that QSs offer superior accuracy, with a lower average error and a smaller standard deviation compared to CSs, indicating better measurement stability. For this comparison, we utilized Python scripts to conduct simulations and statistical analyses, leading to precise and reproducible results. The sensor performance was simulated in a controlled environment, and the obtained data were compared with experimental results. This comparison reveals that QSs are more reliable for applications requiring high precision, such as those in the Internet of Things (IoT) domain. These findings underscore the potential advantage of QSs in critical systems where measurement accuracy is paramount.

System-in-Package (SiP) technology integrates processors, memory stacks, and radio-frequency modules within millimeter-scale enclosures, generating localized thermal peaks that passive cooling cannot address and that are too costly for finite-element solvers to track in real time. Machine learning offers a tractable alternative, but ensemble and generative families have not been jointly evaluated for this task. This work has three objectives: (1) to assess whether ensemble and generative models can predict node-level temperatures in SiP modules with surrogate-grade accuracy; (2) to quantify how a structural mismatch between a generative training objective and a deterministic regression task affects prediction quality; and (3) to identify the family offering the best trade-off between accuracy, computational efficiency, and interpretability. Three paradigms are compared on a finite-element dataset of 10,201 nodes: Random Forest, Extreme Gradient Boosting, and a Variational Autoencoder using normalized three-dimensional coordinates as inputs. Random Forest delivers the strongest accuracy (mean squared error $0.098$ °C²; coefficient of determination $0.997$); Extreme Gradient Boosting attains the lowest inference latency ($0.0044$ ms per node, $0.8$ MB); the Variational Autoencoder incurs a two-orders-of-magnitude regression penalty consistent with its generative objective but preserves a temperature-coherent latent geometry. Ensemble methods are recommended for accurate, interpretable thermal prediction, while the Variational Autoencoder suits downstream anomaly detection.