Nowadays, robots have been widely used in various manufacturing assemblies. However, in the mobile phone flexible printed circuit (FPC) assembly task, robots are still far from trustworthy because the connector and receiver of FPC are very small in size, and detection for them is influenced by the partial occlusion of the robot end manipulator in operation. This makes it difficult to measure the spatial relationship between the connector and receiver in robot picking and buckling. To this end, this work proposes a Digit Twin (DT)-driven measurement method for partial occlusion FPC assembly. The contributions include: (1) we introduce a homography-based strategy to calibrate the FPC placement panel and its virtual placement panel in digit twin. It makes the partial occlusion views in physical space visible in digital twin within tolerable errors. This helps to improve the measurement by eliminating the observation occlusion which originally exits in physical space; (2) we propose a DT-driven reward function measured by the distance and rotation difference between the FPC connector hold by the manipulator and its ideal position in the digital twin. This makes the deep architecture can learn the actions guided by the measurements of the FPC positions in physical space and digital twin simultaneously. The experiments are carried out on a practical assembly platform for the mobile phone Redmi Note 11. With the number of failed trails limited, the proposed network obviously outperforms the pure vision-based measurement and the network without DT support. The proposed method provides a primitive but novel measurement strategy for mobile phone FPC assembly. It can be applied to the real mobile phone assembly lines to reduce the labor burden in the near future.