Adaptive Weighted Error-Correction Method Based on the Error Distribution Characteristics of Multi-Channel Alignment.

As process nodes of advanced integrated circuits continue to decrease below 10 nm, the requirement for overlay accuracy is becoming stricter. The alignment sensor measures the position of the alignment mark relative to the wafer; thus, sub-nanometer alignment position accuracy is vital. The Phase Grating Alignment (PGA) method is widely used due to its high precision and stability. However, the alignment error caused by the mark asymmetry is the key obstacle preventing PGA technology from achieving sub-nanometer alignment accuracy. This error can be corrected using many methods, such as process verification and multi-channel weighted methods based on multi-diffraction, multi-wavelength and multi-polarization state alignment sensors. However, the mark asymmetry is unpredictable, complex and difficult to obtain in advance. In this case, the fixed-weight method cannot effectively reduce the alignment error. Therefore, an adaptive weighted method based on the error distribution characteristic of a multi-channel is proposed. Firstly, the simulation result proves that the error distribution characteristic of the multi-alignment result has a strong correlation with the mark asymmetry. Secondly, a concrete method of constructing weight values based on error distribution is described. We assume that the relationship between the weight value of each channel and the deviations of all channels' results is second-order linear. Finally, without other prior process correction in the simulation experiment, the residual error's Root Mean Square (RMS) of fixed weighted method is 14.0 nm, while the RMS of the adaptive weighted method is 0.01 nm, when dealing with five typical types of mark asymmetry. The adaptive weighted method exhibits a more stable error correction effect under unpredictable and complicated mark asymmetry.

Circular Dichroism Second-Harmonic Generation Imaging of KTiOPO4 Nanocrystal Through Stratified Media.

Potassium titanyl phosphate (KTiOPO4, KTP) particle of nanometric size (nano-KTP) is an attractive material for nonlinear microscopy, and the optimized growth of large-size KTP single crystals has numerous applications for efficient frequency conversion in laser technology. Its three-dimensional orientation and nanoscale morphology are important for growth optimization. In this paper, we introduce an imaging technique based on circular dichroism second-harmonic generation (CD-SHG) to characterize the 3D distribution of KTP nanocrystal. A rigorous theoretical model of CD-SHG imaging for nano-KTP through stratified media is demonstrated. Circular dichroism analysis is used to probe the orientation of 3-axis with respect to the optical observation axis. The research results show that the azimuthal angle of the peak value (SHG) or valley value (CD-SHG) is strongly related to the excitation polarization when the KTP sample is excited by different circular polarizations. Importantly, the refractive index mismatches and the imaging depth also affect the azimuthal angle. Thus, the proposed framework enables a more precise quantitative analysis of the CD-SHG signal of KTP.

Simulation-driven learning: a deep learning approach for image scanning microscopy via physical imaging models.

Image reconstruction based on deep learning has become an effective tool in fluorescence microscopy. Most deep learning reconstruction methods ignore the mechanism of the imaging process where a large number of datasets are required. In addition, a lot of time is spent solving the aliasing problem from multi-scaled image pairs for data pre-processing. Here we demonstrate an improved generative adversarial network for image scanning microscopy (ISM) that can be trained by simulation data and has good generalization. Based on physical imaging models, this method can generate matching image pairs from simulation images and uses them as datasets for network training, without capturing a large number of real ISM images and avoiding image alignment preprocessing. Simulation and experimental results show that this simulation data-driven method improves the imaging quality of conventional microscopic images and reduces the cost of experiments. This method provides inspiration for optimizing network generalizability of the deep learning network.

Deep learning enables confocal laser-scanning microscopy with enhanced resolution.

Theoretical resolution enhancement of confocal laser-scanning microscopy (CLSM) is sacrificed for the best compromise between optical sectioning and the signal-to-noise ratio (SNR). The pixel reassignment reconstruction algorithm can improve the effective spatial resolution of CLSM to its theoretical limit. However, current implementations are not versatile and are time-consuming or technically complex. Here we present a parameter-free post-processing strategy for laser-scanning microscopy based on deep learning, which enables a spatial resolution enhancement by a factor of ∼1.3, compared to conventional CLSM. To speed up the training process for experimental data, transfer learning, combined with a hybrid dataset consisting of simulated synthetic and experimental images, is employed. The overall resolution and SNR improvement, validated by quantitative evaluation metrics, allowed us to correctly infer the fine structures of real experimental images.

Image scanning microscopy with a long depth of focus generated by an annular radially polarized beam.

Image scanning microscopy (ISM) is a promising tool for bioimaging owing to its integration of signal to noise ratio (SNR) and super resolution superior to that obtained in confocal scanning microscopy. In this paper, we introduce the annular radially polarized beam to the ISM, which yields an axially extended excitation focus and enhanced resolution, providing a new possibility to obtain the whole information of thick specimen with a single scan. We present the basic principle and a rigorous theoretical model for ISM with annular radially polarized beam (ISM-aRP). Results show that the resolution of ISM-aRP can be enhanced by 4% compared with that in conventional ISM, and the axial extent of the focus is longer than 6λ. The projected view of the simulated fluorescent beads suspension specimen demonstrates the validity of ISM-aRP to obtain the whole information of volume sample. Moreover, this simple method can be easily integrated into the commercial laser scanning microscopy systems.

Correction of refractive index mismatch-induced aberrations under radially polarized illumination by deep learning.

Radially polarized field under strong focusing has emerged as a powerful manner for fluorescence microscopy. However, the refractive index (RI) mismatch-induced aberrations seriously degrade imaging performance, especially under high numerical aperture (NA). Traditional adaptive optics (AO) method is limited by its tedious procedure. Here, we present a computational strategy that uses artificial neural networks to correct the aberrations induced by RI mismatch. There are no requirements for expensive hardware and complicated wavefront sensing in our framework when the deep network training is completed. The structural similarity index (SSIM) criteria and spatial frequency spectrum analysis demonstrate that our deep-learning-based method has a better performance compared to the widely used Richardson-Lucy (RL) deconvolution method at different imaging depth on simulation data. Additionally, the generalization of our trained network model is tested on new types of samples that are not present in the training procedure to further evaluate the utility of the network, and the performance is also superior to RL deconvolution.

Rigorous modelling of second harmonic generation imaging through stratified media focused by radially polarized beams.

For second harmonic generation (SHG) imaging, the specimen is often observed through an immersion medium and a cover glass whose refractive indices are usually different from that of the specimen. However, the currently used theoretical models are based on the assumption that the specimen is situated in a homogeneous medium. The limitation of these models is that they ignore the effects of the refractive index mismatches and the imaging depth. In this paper, we have demonstrated, for the first time to our knowledge, a rigorous model of SHG imaging through stratified media focused by radially polarized beams. Based on the proposed model, the detected SHG intensity patterns excited in a refractive index perfectly matched, aberration-free medium and in mismatched stratified media are compared. The effects of the imaging depth and effective numerical aperture (NA) on the performance of SHG imaging with oil immersion objectives are investigated by the stratified media model. It is found that the full width at half maximum (FWHM) in the axial direction at imaging depth of 80 µm is ~3.1 times as large as that of 10 µm imaging depth. While for the transverse FWHM, the increment is only about 23%. The quality of the SHG intensity distribution can be increased by reducing the NA appropriately at the expense of the detected signal strength. The proposed model is helpful to provide guidelines for the adaptive aberration correction in SHG imaging and can be used to optimize the experimental configuration.