Scanning probe lithography (SPL) is a versatile nanofabrication method that employs a scanning probe microscope (SPM) to generate patterns and nanoscale structures on surfaces. Typically, an atomic force microscope (AFM) is the preferred type of SPM for nanolithography and in situ characterization based on the probe–sample interaction. However, the maximum area of the existing SPL is mainly limited by scanner stroke of the AFM and usually less than $100\times 100$ μm $^{2}$ . Ultralarge-area nanofabrication can be achieved by using a “step and scan” manner but leading to stitching errors and low throughput. This article proposes a novel ultralarge-area stitchless SPL and high-throughput in situ characterization system utilizing a leaf spring-based nanomanipulator, which offers a maximum scanning area of $2\times 2$ mm $^{2}$ . Further, we propose a novel optimized passband loss filter for the repetitive control of the nanomanipulator to realize high-bandwidth and high-precision trajectory tracking. Experimental results indicate that the proposed control method achieves satisfactory tracking performance for a triangular wave with an amplitude of 500 μm. Compared with the existing SPL systems, we achieve stitchless nanolithography at a speed of $\sim $ 2 mm/s and high-throughput in situ characterization in the range of $500\times 500$ μm $^{2}$ . This system opens up significant avenues for the research and application of ultralarge-area nanofabrication and in situ characterization.

Hybrid reluctance actuators, known for their remarkable motor constant and bidirectional noncontact force, emerge as superior alternatives to piezoelectric stack or voice coil actuators. A key challenge in large-stroke ( $>1$ mm) hybrid reluctance actuators is their inherent nonlinearity characterized by fluctuations in negative stiffness and motor constant. To tackle this problem, we propose a large-stroke reluctance-actuated nanopositioner by leveraging on a compliant stiffness compensator to reduce the adverse impact of the reluctance actuator's nonlinearity. We establish the relationship between the system's equivalent stiffness and the combined effects of the reluctance actuator and compliant compensator. Based on a novel nonlinear decoupling mechanism, the fluctuation in the system's equivalent stiffness is significantly reduced, enabling advanced model-based controls and facilitating high precision motion. A prototype system compatible with atomic force microscopy is established, and the system's performance is validated using a repetitive control with the recently developed optimized passband loss filter, demonstrating nanometric precision in large-range and high-frequency scanning. The experimental results reveal that the proposed system achieves a precision of 17.6 nm (RMSE) for a 2 mm triangular wave at 1 Hz and 8.2 nm (RMSE) for a $20\,\mu$ m triangular wave at 80 Hz. Further validation through atomic force microscopy confirms the system's capability in large-range and high-speed characterization. These results suggest that the proposed system could significantly advance the use of large-stroke reluctance-actuated nanopositioners for millimeter-range applications.

Scanning probe lithography (SPL) is a promising technology to fabricate high-resolution, customized and cost-effective features at the nanoscale. However, the quality of nano-fabrication, particularly the critical dimension, is significantly influenced by various SPL fabrication techniques and their corresponding process parameters. Meanwhile, the identification and measurement of nano-fabrication features are very time-consuming and subjective. To tackle these challenges, we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning (ML). Different from traditional SPL techniques that rely on manual labeling-based experimental methods, the proposed framework intelligently extracts reliable and global information for statistical analysis to fine-tune and optimize process parameters. Based on the proposed framework, we realized the processing of smaller critical dimensions through the optimization of process parameters, and performed direct-write nano-lithography on a large scale. Furthermore, data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.

Micro-stereolithography ($\mu$SL) is an advanced additive manufacturing technique that enables the fabrication of highly precise microstructures with fine feature resolution. One of the primary challenges in $\mu$SL is achieving and maintaining precise focus throughout the fabrication process. For the successful application of $\mu$SL, it is essential to maintain the sample surface within a focal depth of several microns. Despite the growing interest in auto-focus devices, limited attention has been directed towards auto-focus systems in image-based auto-focus microscope systems for precision $\mu$SL. To address this challenge, we propose an image-based auto-focus microscope system incorporating visual servo control. In the optical design, a transflective beam splitter is employed, allowing the laser beam to pass through for fabrication while reflecting the focused beam on the sample surface to the microscope and camera. Utilizing captured spot images and the Foucault knife-edge test, a deep learning-based laser spot image processing algorithm is developed to determine the focus position based on spot size and the number of spot pixels on both sides. Experimental results demonstrate that the proposed auto-focus system effectively determines the relative position of the focal point using the laser spot image and achieves auto-focusing through visual servo control.