Search

The ferroelectric PZT thin films were prepared on Pt/Ti/SiO2/Si substrate by RF sputtering method followed by the rapid thermal annealing. The preparation of the Pt and Ti thin films as bottom electrode, and their influences on the PZT thin films were studied in details. The substrate temperature during sputtering was room temperature; the rapid thermal annealing temperature was 500°C-750°C and the annealing time was 30-70s. The influences of different preparation parameters on the structure and electric properties were studied with X-ray diffraction technique and RT66A Standardized Ferroelectric Test System. The electric properties of the prepared PZT thin film was: Pa=39μc/cm2, Pr = 9.3 μc/cm2, Ec=28KV/mm, ε=300, p=109ω⋅cm.

The temperature dependence of the electromechanical coupling factor Kt in PLZT and PSZT ceramics with the composition situated near the ferroelectric(F) and antiferroelectric(AF) phase boundary is investigated. The results show that the value of Kt is connected with the F-AF phase boundary. The Kt, in PLZT ceramics drops down with the increase of temperature which makes the measured site depart from the F-AF phase boundary. The Kt in PSZT ceramics increases with the increase of temperature which makes the measured site close to the F-AF phase boundary. It is possible to develop the piezoelectric ceramic materials with good temperature stability by means of the choice of composition with the proper F-AF phase boundary.

Background: Convolutional neural networks (CNNs) have been extensively applied to two-dimensional (2D) medical image segmentation, yielding excellent performance. However, their application to three-dimensional (3D) nodule segmentation remains a challenge. Methods: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image. Results: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072. Conclusion: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.