深度学习驱动的微流体芯片架构设计,用于智能粒子运动控制
Hongxia Li1, Xuhui Chen1, Du Qiao1
1State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian 116023, China.
Lab on a chip
|January 27, 2026
概括
我们开发了一个深度学习框架,用于设计微流体通道网络 (MCN). 该系统可快速自动设计MCN,用于在芯片实验室应用中精确处理颗粒.
科学领域:
- 微流体学 微流体学
- 人工智能的人工智能
- 生物技术是生物技术.
背景情况:
- 设计复杂的微流体通道网络 (MCNs) 进行精确的粒子操纵是具有挑战性的.
- 目前的方法很难将所需的粒子轨迹转化为可制造的设备设计.
研究的目的:
- 为自动化MCN设计引入一个模块化深度学习框架.
- 为了使微流体设备中的粒子能够快速而精确地进行时空控制.
主要方法:
- 将MCN分解为标准化,可重复使用的功能模块.
- 使用专用神经网络来预测每个模块内的粒子状态 (位置,速度,传输时间).
- 一个多模块重配置算法 (MMRA) 将本地预测组装成设备规模的轨迹,确保物理状态的连续性.
主要成果:
- 该框架允许确定性端口路由和精确的时空调度,平均绝对时间误差低于0.031秒.
- 集成到PathChip平台允许自动生成优化的模块序列,几何形状和控制参数.
- 多达5000个模块的设计可以在18秒内生成.
结论:
- 这项工作为微流体学中可编程,设备级的时空粒子操纵提供了一个可扩展的方法.
- 该框架对芯片实验室自动化,高通量选和自适应性微流体系统具有重大影响.
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