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Microscale temperature sensing and control are essential in various applications. Thermistors are widely utilized for temperature sensing owing to their simple design and high sensitivity. The future of thermistors lies in miniaturization and integration in highly customizable and sustainable electronics. Moreover, the ever-reducing size of the transistors requires the thermistors to scale down proportionally, without any compromise to its functionality. However, fabricating microscale thermistors exhibiting high accuracy and repeatable measurements has been challenging for state-of-the-art device miniaturization. Here, we develop versatile printing of microscale thermistors from silver fluoride solution by exploiting laser-induced opto-thermal microbubbles. The microscale temperature gradients on the bubble surface create an enhanced concentration of silver ions around the bubble to enable high-resolution printing of submicrometer structures with low-concentration precursors and low wastage. We demonstrate the bubble printing of thermistor arrays with both positive and negative thermal coefficients by exploiting the size effect on the electrical conductivity. We further investigate the sensing performance and long-term stability of the printed thermistors and conclude that the bubble-printed thermistors exhibit high-resolution sensing capabilities with long-term stability, promising enhanced performance in medical and semiconductor applications. 

Abstract Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML‐based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science, and ML.