Diffractive gratings are often associated with the use of beam shaping device utilizing a monochromatic source. They could provide high flexibility in design and offer a precise control according to applications. The design of diffraction grating often makes several iterations through design and prototyping before completion. In this paper, we demonstrate a computer-aided design method and a mechanical method of prototyping diffractive grating optics for beam shaping, which aim at improve productivity through greater design flexibility, rapid fabrication and cost reduction. A description of the optical design is presented along with a discussion on the integrated patterning system.

A system, employing the probe of an atomic force microscope to mechanically pattern various materials such as photoresist, semiconductors or polymers in the nanometer regime has been developed. The system was utilized for characterization of nanowires including carbon nanotubes (CNTs) and silicon nanowires (SiNWs)

This paper describes a virtual reality interface between human and the Atomic Force Microscope (AFM), which allows the operator to perform nanomanipulation with an AFM tip in the virtual reality environment with haptic feedback. During operation, the tip-sample interaction forces and intermolecular forces between the tip and surface are modeled based on Lennard-Jones potential and JKR theory, respectively. Our objective is to provide a 3D virtual reality interface capable of displaying topography of surface for the users and allow them to predict the results for the manipulation.

Grasping-force optimization of multifingered robotic hands can be formulated as a problem for minimizing an objective function subject to form-closure constraints and balance constraints of external force. This paper presents a novel recurrent neural network for real-time dextrous hand-grasping force optimization. The proposed neural network is shown to be globally convergent to the optimal grasping force. Compared with existing approaches to grasping-force optimization, the proposed neural-network approach has the advantages that the complexity for implementation is reduced, and the solution accuracy is increased, by avoiding the linearization of quadratic friction constraints. Simulation results show that the proposed neural network can achieve optimal grasping force in real time.