In the new age of illumination, light emitting diodes (LEDs) have been proven to be the most efficient alternative to conventional light sources. Yet, in comparison to other lighting systems, LEDs operate at low temperatures while junction temperature (T_j) is is among the main factors dictating their lifespan, reliability, and performance. This indicates that accurate measurement of LED temperature is of great importance to better understand the thermal effects over a system and improve performance. Over the years, various T_j measurement techniques have been developed, and existing methods have been improved in many ways with technological and scientific advancements. Correspondingly, in order to address the governing phenomena, benefits, drawbacks, possibilities, and applications, a wide range of measurement techniques and systems are covered. This paper comprises a large number of published studies on junction temperature measurement approaches for LEDs, and a summary of the experimental parameters employed in the literature are given as a reference. In addition, some of the corrections noted in non-ideal thermal calibration processes are discussed and presented. Finally, a comparison between methods will provide the readers a better insight into the topic and direction for future research.

While junction temperature control is an indispensable part of having reliable solid-state lighting, there is no direct method to measure its quantity. Among various methods, temperature-sensitive optical parameter-based junction temperature measurement techniques have been used in practice. Researchers calibrate different spectral power distribution behaviors to a specific temperature and then use that to predict the junction temperature. White light in white LEDs is composed of blue chip emission and down-converted emission from photoluminescent particles, each with its own behavior at different temperatures. These two emissions can be combined in an unlimited number of ways to produce diverse white colors at different brightness levels. The shape of the spectral power distribution can, in essence, be compressed into a correlated color temperature (CCT). The intensity level of the spectral power distribution can be inferred from the luminous flux as it is the special weighted integration of the spectral power distribution. This paper demonstrates that knowing the color characteristics and power level provide enough information for possible regressor trainings to predict any white LED junction temperature. A database from manufacturer datasheets is utilized to develop four machine learning-based models, viz., k-Nearest Neighbor (KNN), Radius Near Neighbors (RNN), Random Forest (RF), and Extreme Gradient Booster (XGB). The models were used to predict the junction temperatures from a set of dynamic opto-thermal measurements. This study shows that machine learning algorithms can be employed as reliable novel prediction tools for junction temperature estimation, particularly where measuring equipment limitations exist, as in wafer-level probing or phosphor-coated chips.

Acoustic holographic lenses, also known as acoustic holograms, can change the phase of a transmitted wavefront in order to shape and construct complex ultrasound pressure fields, often for focusing the acoustic energy on a target region. These lenses have been proposed for transcranial focused ultrasound (tFUS) to create diffraction-limited focal zones that target specific brain regions while compensating for skull aberration. Holograms are currently designed using time-reversal approaches in full-wave time-domain numerical simulations. Such simulations need time-consuming computations, which severely limits the adoption of iterative optimization strategies. In the time-reversal method, the number and distribution of virtual sources can significantly influence the final sound field. Because of the computational constraints, predicting these effects and determining the optimal arrangement is challenging. This study introduces an efficient method for designing acoustic holograms using a volumetric holographic technique to generate focused fields inside the skull. The proposed method combines a modified mixed-domain method for ultrasonic propagation with a gradient descent iterative optimization algorithm. The findings are further validated in underwater experiments with a realistic 3D-printed skull phantom. This approach enables substantially faster holographic computation than previously reported techniques. The iterative process uses explicitly defined loss functions to bias the ultrasound field’s optimization parameters to specific desired characteristics, such as axial resolution, transversal resolution, coverage, and focal region uniformity, while eliminating the uncertainty associated with virtual sources in time-reversal techniques. The proposed techniques enable more rapid hologram computation and more flexibility in tailoring ultrasound fields for specific therapeutic requirements.

Acoustic holographic lenses (AHLs) show great potential as a straightforward, inexpensive, and reliable method of sound manipulation. These lenses store the phase and amplitude profile of the desired wavefront when illuminated by a single acoustic source to reconstruct ultrasound pressure fields, induce localized heating, and achieve temporal and spatial thermal effects in acousto-thermal materials like polymers. The ultrasonic energy is transmitted and focused by AHL from a transducer into a particular focal volume. It is then converted to heat by internal friction in the polymer chains, causing the temperature of the polymer to rise at the focus locations while having little to no effect elsewhere. This one-of-a-kind capability is made possible by the development of AHLs to make use of the translation of attenuated pressure fields into programmable heat patterns. However, the impact of acousto-thermal dynamics on the generation of AHLs is largely unexplored. We use a machine learning-assisted single inverse problem approach for rapid and efficient AHLs’ design to generate thermal patterns. The process involves the conversion of thermal information into a holographic representation through the utilization of two latent functions: pressure phase and amplitude. Experimental verification is performed for pressure and thermal measurements. The volumetric acousto-thermal analyses of experimental samples are performed to offer a knowledge of the obtained pattern dynamics, as well as the applicability of holographic thermal mapping for precise volumetric temperature control. Finally, the proposed framework aims to provide a solid foundation for volumetric analysis of acousto-thermal patterns within thick samples and for assessing thermal changes with outer surface measurements.