Hybrid physics-guided data-driven modeling for generalizable geometric accuracy prediction and improvement in two-photon lithography

Sixian Jia, Jieliyue Sun, Andrew Howes, Michelle R. Dawson, Kimani C. Toussaint, Chenhui Shao

Research output: Contribution to journalArticlepeer-review


Two-photon lithography (TPL) is an additive manufacturing technique to produce three-dimensional (3D) micro- and nano-scale structures. Geometric accuracy is of vital importance to ensure the quality and functionality of additively manufactured 3D structures. However, there exists limited research on modeling and improving the geometric accuracy of TPL. Moreover, there is a lack of understanding of the influence of TPL process parameters (e.g., scanning rate, laser power) and structure design on the resulting geometric accuracy. This paper presents a new hybrid physics-guided data-driven modeling framework for generalizable cross-design and cross-parameter geometric accuracy prediction and improvement. The proposed model decomposes TPL-produced geometric features into two levels: a global trend and a spatial trend. Guided by physical laws that govern voxel dimensions, we construct the global trend as a function of TPL process parameters. Our new findings reveal strong linear relationships among the parameters of the global trend model across structure design dimensions, which enable generalizable predictive modeling. Moreover, the spatial trend captures the spatially varying patterns in geometric features. A large-scale experimental design consisting of six hemisphere sizes and six parameter combinations is carried out to thoroughly test the effectiveness of the proposed method. Our modeling approach demonstrates high accuracy in predicting the geometric error of a target design with knowledge derived from other designs and parameters. The average prediction errors for radius and height are 5.23% and 4.66%, respectively. Furthermore, the proposed compensation strategy is shown to reduce the geometric errors from 22.19% to 3.21% for radius, and from 12.18% to 4.96% for height. Meanwhile, the within-sample variations are greatly reduced, indicating improved process consistency. To the best of our knowledge, this study is among the first to develop a generalizable method for cross-design and cross-parameter geometric accuracy modeling and improvement in TPL.

Original languageEnglish (US)
Pages (from-to)202-210
Number of pages9
JournalJournal of Manufacturing Processes
StatePublished - Jan 31 2024


  • Additive manufacturing
  • Generalizability
  • Geometric accuracy
  • Physics-guided machine learning
  • Quality control
  • Two-photon lithography

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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