Size-based separation of bioparticles/cells is crucial to a variety of
biomedical processing steps for applications such as exosomes and DNA
isolation. Design and improvement of such microfluidic devices is a challenge
to best answer the demand for producing homogeneous end-result for study and
use. Deterministic lateral displacement (DLD) exploits a similar principle that
has drawn extensive attention over years. However, the lack of predictive
understanding of the particle trajectory and its induced mode makes designing a
DLD device an iterative procedure. Therefore, this paper investigates a fast
versatile design automation platform to address this issue. To do so,
convolutional and artificial neural networks were employed to learn velocity
fields and critical diameters of a wide range of DLD configurations. Later,
these networks were combined with a multi-objective evolutionary algorithm to
construct the automation tool. After ensuring the accuracy of the neural
networks, the developed tool was tested for 12 critical conditions. Reaching
the imposed conditions, the automation components performed reliably with
errors of less than 4%. Moreover, this tool is generalizable to other
field-based problems and since the neural network is an integral part of this
method, it enables transfer learning for similar physics. All the codes
generated and used in this study alongside the pre-trained neural network
models are available on this https URL.

本研究探讨了基于微流控芯片的确定性侧向位移分离操作，利用卷积神经网络和人工神经网络学习速度场和关键直径，并结合多目标进化算法构建自动化设计工具。经过测试，在 12 个关键条件下，该自动化组件表现可靠，误差小于 4％。此工具具有广泛适用性，可推广用于其他领域性问题，且可用于类似物理学习的转移学习。