Physics-based differentiable simulation framework for power optimization in parallel two-photon polymerization

Two-photon polymerization (TPP) enables high-resolution additive manufacturing at the microscale, but accurate control of voxel shape remains a challenge due to nonlinear exposure dynamics and complex resist behavior. We present a physics-based differentiable model of the TPP process that includes radical generation, oxygen quenching, polymerization and development. The model is implemented in PyTorch and allows optimization of laser power using gradient-based backpropagation. Experimental validation is performed using a setup with a diffractive optical element (DOE) that enables parallel voxel exposure. This parallelization improves throughput but introduces proximity effects when several voxels are written close to each other. We show that the optimization technique compensates for these effects, resulting in better agreement between simulated and printed structures.