用蜻蜓翅膀结构设计微流控芯片，实现低功耗高效模式识别
Insect-Wing Structured Microfluidic System for Reservoir Computing
- 模仿蜻蜓翅膀的微通道网络，用液体流动编码输入信号
- 在数据有限情况下仍达91%分类准确率，且分辨率较粗
- 适合极端环境下的低功耗计算，如生物医疗或野外传感
随着对更高效、自适应计算的需求增长，仿生架构为传统电子设计提供了有前景的替代方案。微流控平台借鉴生物形态与流体动力学，为电子不适用环境中的低功耗、高鲁棒性计算提供了有力基础。本研究探索了一种基于蜻蜓翅结构的混合储层计算系统，通过微流控芯片将时间输入模式编码为微通道网络内的流体相互作用。系统采用三个染料输入通道和三个摄像头监控的检测区域，将离散空间模式转化为动态颜色输出信号。这些储层输出信号经处理后送入简单可训练的读出层进行模式分类。结合原始储层输出与合成输出，评估了系统性能、清晰度和数据效率。结果表明，在粗分辨率和有限训练数据条件下，分类准确率最高可达91%，验证了微流控储层计算的可行性。
原文摘要 · Abstract (English)
As the demand for more efficient and adaptive computing grows, nature-inspired architectures offer promising alternatives to conventional electronic designs. Microfluidic platforms, drawing on biological forms and fluid dynamics, present a compelling foundation for low-power, high-resilience computing in environments where electronics are unsuitable. This study explores a hybrid reservoir computing system based on a dragonfly-wing inspired microfluidic chip, which encodes temporal input patterns as fluid interactions within the micro channel network. The system operates with three dye-based inlet channels and three camera-monitored detection areas, transforming discrete spatial patterns into dynamic color output signals. These reservoir output signals are then modified and passed to a simple and trainable readout layer for pattern classification. Using a combination of raw reservoir outputs and synthetically generated outputs, we evaluated system performance, system clarity, and data efficiency. The results demonstrate consistent classification accuracies up to $91\%$, even with coarse resolution and limited training data, highlighting the viability of the microfluidic reservoir computing.
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