Smart healthcare systems integrated with advanced deep neural networks enable real‐time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND‐type flash memory array with a rounded double channel for computing‐in‐memory (CIM) architecture to overcome the limitations of conventional smart healthcare systems: the necessity of high area and energy efficiency while maintaining high classification accuracy is proposed. The fabricated array, characterized by low‐power operations and high scalability with double independent channels per floor, exhibits enhanced cell density and energy efficiency while effectively emulating the features of biological synapses. The CIM architecture leveraging the fabricated array achieves high classification accuracy (93.5%) for electrocardiogram signals, ensuring timely detection of potentially life‐threatening arrhythmias. Incorporated with a simplified spike‐timing‐dependent plasticity learning rule, the CIM architecture is suitable for robust, area‐ and energy‐efficient in‐memory arrhythmia detection systems. This work effectively addresses the challenges of conventional smart healthcare systems, paving the way for a more refined healthcare paradigm.

中文翻译：

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内存生物信号分析演示：用于心律失常检测的新型高密度和低功耗 3D 闪存阵列

智能医疗系统与先进的深度神经网络集成，可实现实时健康监测、早期疾病检测和个性化治疗。在这项工作中，一种新颖的3D AND型闪存阵列具有圆形双通道内存计算（CIM）架构，以克服传统智能医疗系统的局限性：在保持高分类的同时需要高面积和能源效率提出了准确度。该阵列具有低功耗运行和高可扩展性的特点，每层有双独立通道，在有效模拟生物突触特征的同时，表现出增强的细胞密度和能源效率。利用制造的阵列的 CIM 架构实现了心电图信号的高分类精度 (93.5%)，确保及时检测出可能危及生命的心律失常。 CIM 架构结合了简化的尖峰时序依赖性可塑性学习规则，适用于稳健、面积和能源高效的内存心律失常检测系统。这项工作有效地解决了传统智能医疗系统的挑战，为更精细的医疗保健范式铺平了道路。