Micro-electro-mechanical systems (MEMS)-based sensors endure complex production processes that inherently include high variance. To meet rigorous client demands (such as sensitivity, offset noise, robustness against vibration, etc.). products must go through comprehensive calibration and testing procedures. All sensors undergo a standardized and sequential calibration process with a predetermined number of steps, even though some may reach the correct calibration value sooner. Moreover, the traditional sequential calibration method faces challenges due to specific operating conditions resulting from manufacturing discrepancies. This not only extends the calibration duration but also introduces rigidity and inefficiency. To tackle the issue of production variances and elongated calibration time and enhance efficiency, we provide a novel quasi-parallelized calibration framework aided by an artificial intelligence (AI) based solution. Our suggested method utilizes a supervised tree-based regression technique and statistical measures to dynamically identify and optimize the appropriate working point for each sensor. The objective is to decrease the total calibration duration while ensuring accuracy. The findings of our investigation show a time reduction of 23.8% for calibration, leading to substantial cost savings in the manufacturing process. In addition, we propose an end-to-end monitoring system to accelerate the incorporation of our framework into production. This not only guarantees the prompt execution of our solution but also enables the identification of process modifications or data irregularities, promoting a more agile and adaptable production process.

基于微机电系统 (MEMS) 的传感器需要承受复杂的生产过程，而生产过程本身就存在很大的差异。满足严格的客户要求（例如灵敏度、偏置噪声、抗振动鲁棒性等）。产品必须经过全面的校准和测试程序。所有传感器都经过预定步骤数的标准化和顺序校准过程，尽管有些传感器可能会更快达到正确的校准值。此外，由于制造差异导致的特定操作条件，传统的顺序校准方法面临着挑战。这不仅延长了校准持续时间，而且还带来了僵化和低效率。为了解决生产差异和校准时间延长的问题并提高效率，我们提供了一种新颖的准并行校准框架，并辅以基于人工智能（AI）的解决方案。我们建议的方法利用基于树的监督回归技术和统计测量来动态识别和优化每个传感器的适当工作点。目标是在确保准确性的同时减少总校准持续时间。我们的调查结果显示，校准时间减少了 23.8%，从而大大节省了制造过程中的成本。此外，我们提出了一个端到端监控系统，以加速将我们的框架纳入生产。这不仅保证了我们的解决方案的及时执行，而且还能够识别流程修改或数据异常，促进生产流程更加敏捷和适应性更强。