低功耗高性能智能化气体传感器系统及其应用研究

      气体传感器在新能源、医疗、电力等多个关键行业中扮演着重要角色，尤其在
环境监测、智能家居和健康监测等领域。随着对气体传感器性能要求的提升，市场
对低功耗、智能化和高性能传感器的需求日益增长。本文针对气体传感器在发展
中遇到的挑战，如如何实现低功耗气体传感、机器学习算法的理论指导缺乏、高
精度多任务检测难题以及边缘端设备算法部署限制，提出了一系列创新解决方案。
       首先，本文第二章介绍了一种基于多壁碳纳米管（MWCNTs）和聚苯胺（PANI）
的室温氨气传感器，这种传感器在PET 基底上通过柔性电子打印技术制备叉指电
极，随后涂覆敏感材料并进行干燥处理，形成具有粗糙表面和多孔结构的传感器，
实现了低功耗气体检测。但是室温检测的方法容易受到环境因素的干扰。因此，提
出了采用脉冲加热技术，更加高效的利用金属氧化物气体传感器的微加热板的加
热能力，将传统气体传感的功耗从几十毫瓦降低到十毫瓦以内，实现了一种低功
耗下的高温气体传感方式。
       在第三章中，提出了基于脉冲加热的测试方法，在低功耗的基础上，提高气体
传感器的信号稳定性和精确度。脉冲加热模式下的传感器响应具有良好的可重复
性和独立性，有助于减少测试过程中的干扰，提升数据集的准确性和可靠性。通
过表面态捕获模型解析脉冲加热下电导变化与微观物理参数的关系，提取与气体
浓度相关的参数速率𝛽，虽然直接通过𝛽 进行浓度回归的准确性有限，但其有助
于构建更准确的浓度回归模型。结合机器学习算法，特别是深度学习模型，可以
有效融合𝛽 参数和其他传感器数据，实现气体浓度的精确识别和预测。
      第四章在单脉冲气体浓度检测的基础下，进一步提出了基于多脉冲测试数据
的双损失函数模型和联合模型，针对传统算法在同时检测气体浓度和种类方面的
不足，双损失函数模型通过调整损失函数权重，优先提升分类精度，再提升回归精
度，以提高模型准确性。联合模型则通过将分类和回归任务分配给两个深度学习
模型，并采用级联或模型选择的方式整合，实现了更高效的气体检测。
      最后，基于前四章的研究基础，第五章介绍了基于脉冲方法的晶圆级气体传
感器质检系统和无线智能气体传感系统。晶圆级系统利用脉冲加热快速测试的特
性，通过高效的片上检测提升了生产效率并降低了成本。无线智能气体传感系统
则通过将先前学习得到的深度学习模型部署到云端，实现了低功耗、智能化、高
性能的气体传感，推动了气体传感器在低功耗和智能化方面的功能发展，满足了生产和生活中对气体传感器系统的更多需求。
       本文针对低功耗、高性能智能气体传感系统的研发，从材料选择、传感器制
备、测试方法优化到机器学习算法设计，开展了一系列深入研究和实践，成功开发
出具有良好性能和应用前景的柔性室温氨气传感器，并提出了基于金属氧化物气
体传感器的脉冲加热和多任务机器学习的先进测试策略，为推动气体传感技术的
智能化、低功耗化发展做出了积极贡献。这些研究成果不仅丰富了气体传感技术
理论，也为未来智能设备、环保监测、健康监测等领域的实际应用提供了有力的
技术支撑。

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