Periodicity-Enforced Neural Network for Designing Deterministic Lateral Displacement Devices

TL;DR

This paper presents a novel neural network architecture with periodic layers that accurately models flow fields in DLD devices, significantly improving design efficiency and boundary condition enforcement for microfluidic applications.

Contribution

It introduces a periodicity-enforced surrogate model with specialized layers that ensure exact boundary matching, advancing DLD device design without penalty-based methods.

Findings

Achieves 0.478% critical diameter prediction error

Maintains perfect periodicity in flow predictions

Improves accuracy by 85.4% over baseline methods

Abstract

Deterministic Lateral Displacement (DLD) devices enable liquid biopsy for cancer detection by separating circulating tumor cells (CTCs) from blood samples based on size, but designing these microfluidic devices requires computationally expensive Navier-Stokes simulations and particle-tracing analyses. While recent surrogate modeling approaches using deep learning have accelerated this process, they often inadequately handle the critical periodic boundary conditions of DLD unit cells, leading to cumulative errors in multi-unit device predictions. This paper introduces a periodicity-enforced surrogate modeling approach that incorporates periodic layers, neural network components that guarantee exact periodicity without penalty terms or output modifications, into deep learning architectures for DLD device design. The proposed method employs three sub-networks to predict steady-state, non-dimensional velocity and pressure fields (u, v, p) rather than directly predicting critical diameters or particle trajectories, enabling complete flow field characterization and enhanced design flexibility. Periodic layers ensure exact matching of flow variables across unit cell boundaries through architectural enforcement rather than soft penalty-based approaches. Validation on 120 CFD-generated geometries demonstrates that the periodic layer implementation achieves 0.478% critical diameter error while maintaining perfect periodicity consistency, representing an 85.4% improvement over baseline methods. The approach enables efficient and accurate DLD device design with guaranteed boundary condition satisfaction for multi-unit device applications.