Bücher Tim, Huber Robert, Eschenbaum Carsten, Mertens Adrian, Lemmer Uli, Amrouch Hussam
University of Stuttgart, Semiconductor Test and Reliability (STAR), Pfaffenwaldring 47, 70569, Stuttgart, Germany.
Karlsruhe Institute for Technology (KIT), Light Technology Institute (LTI), Engesserstrasse 13, 76131, Karlsruhe, Germany.
Sci Rep. 2022 Aug 20;12(1):14231. doi: 10.1038/s41598-022-18321-6.
Fully-printed temperature sensor arrays-based on a flexible substrate and featuring a high spatial-temperature resolution-are immensely advantageous across a host of disciplines. These range from healthcare, quality and environmental monitoring to emerging technologies, such as artificial skins in soft robotics. Other noteworthy applications extend to the fields of power electronics and microelectronics, particularly thermal management for multi-core processor chips. However, the scope of temperature sensors is currently hindered by costly and complex manufacturing processes. Meanwhile, printed versions are rife with challenges pertaining to array size and sensor density. In this paper, we present a passive matrix sensor design consisting of two separate silver electrodes that sandwich one layer of sensing material, composed of poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS). This results in appreciably high sensor densities of 100 sensor pixels per cm[Formula: see text] for spatial-temperature readings, while a small array size is maintained. Thus, a major impediment to the expansive application of these sensors is efficiently resolved. To realize fast and accurate interpretation of the sensor data, a neural network (NN) is trained and employed for temperature predictions. This successfully accounts for potential crosstalk between adjacent sensors. The spatial-temperature resolution is investigated with a specially-printed silver micro-heater structure. Ultimately, a fairly high spatial temperature prediction accuracy of 1.22 °C is attained.
基于柔性基板且具有高空间温度分辨率的全印刷温度传感器阵列在众多学科领域都具有巨大优势。这些领域涵盖医疗保健、质量与环境监测，以及新兴技术，如软机器人技术中的人造皮肤。其他值得注意的应用还扩展到电力电子和微电子领域，特别是多核处理器芯片的热管理。然而，温度传感器的应用范围目前受到成本高昂且复杂的制造工艺的阻碍。同时，印刷版本在阵列尺寸和传感器密度方面存在诸多挑战。在本文中，我们提出了一种无源矩阵传感器设计，它由两个单独的银电极组成，中间夹着一层由聚（3,4 - 乙撑二氧噻吩）：聚苯乙烯磺酸盐（PEDOT:PSS）构成的传感材料。这使得空间温度读数的传感器密度达到每平方厘米100个传感器像素，同时保持了较小的阵列尺寸。因此，有效解决了这些传感器广泛应用的一个主要障碍。为了实现对传感器数据的快速准确解读，我们训练并使用了一个神经网络（NN）进行温度预测。这成功地解决了相邻传感器之间潜在的串扰问题。通过一种特殊印刷的银微加热器结构对空间温度分辨率进行了研究。最终实现了1.22°C的相当高的空间温度预测精度。
