The demand for an ultrabroad optical material with a bandgap tunable from zero to at least 1–2 eV has been one of the driving forces for exploring new 2D materials since the emergence of graphene, transition metal dichalcogenides, and black phosphorus. As an ultra-broadband 2D material with energy bandgap ranging from 0 to 1.2 eV, layered PtSe2 shows much better air stability than its analogue, black phosphorous. In this work, high quality of centimeter scale PtSe2 films with controllable thicknesses were prepared through thermally assisted conversion method. The linear and nonlinear optical performance and ultrafast dynamics of layered PtSe2, and signatures of the transition from semiconductor to semimetal have been systematically studied experimentally and theoretically. Combining with rate equations, first-principles calculation, and electrical measurements, a comprehensive understanding about the evolution of nonlinear absorption and carrier dynamics with increasing layer thickness is provided, indicating its promising potential in nanophotonic devices such as infrared detectors, optical switches, and saturable absorbers.
To achieve airborne wide-field hyperspectral remote sensing, we often use a way of splice two or more detectors. This paper proposes a new whiskbroom hyperspectral imaging system with using a motion structure scanning to obtain a wide-field hyperspectral data. In the meantime, aircraft’s attitude disturbance could be compensated. Compared with field splicing, the system reduces complexity and cost of optical structure. We provide a new method to realize significant airborne wide-field, high-resolution remote sensing spectral imaging.
An efficient pixel-based mask optimization method via particle swarm optimization (PSO) algorithm for inverse lithography is proposed. Because of the simplicity of principles, the ease of implementation and the efficiency of convergence, PSO has been widely used in many fields. In this study, PSO is used to solve the inverse problem of mask optimization. The pixel-based mask patterns are transformed into frequency space using discrete cosine transformation and the frequency components are encoded into particles. The pattern fidelity is adopted as the fitness function to evaluate these particles. The mask optimization method is implemented by updating the velocities and positions of these particles. Simulation results show that the image fidelity has been efficiently improved after using the proposed method.
Source optimization is one of the key techniques for achieving higher resolution without increasing the complexity of mask design. An efficient source optimization approach is proposed on the basis of particle swarm optimization. The pixelated sources are encoded into particles, which are evaluated by using the pattern error as the fitness function. Afterward, the optimization is implemented by updating the velocities and positions of these particles. This approach is demonstrated using three mask patterns, including a periodic array of contact holes, a vertical line/space design, and a complicated pattern. The pattern errors are reduced by 69.6%, 51.5%, and 40.3%, respectively. Compared with the source optimization approach via genetic algorithm, the proposed approach leads to faster convergence while improving the image quality at the same time. Compared with the source optimization approach via gradient descent method, the proposed approach does not need the calculation of gradients, and it has a strong adaptation to various lithographic models, fitness functions, and resist models. The robustness of the proposed approach to initial sources is also verified.
In recent years, with the availability of freeform sources, source optimization has emerged as one of the key techniques for achieving higher resolution without increasing the complexity of mask design. In this paper, an efficient source optimization approach using particle swarm optimization algorithm is proposed. The sources are represented by pixels and encoded into particles. The pattern fidelity is adopted as the fitness function to evaluate these particles. The source optimization approach is implemented by updating the velocities and positions of these particles. The approach is demonstrated by using two typical mask patterns, including a periodic array of contact holes and a vertical line/space design. The pattern errors are reduced by 66.1% and 39.3% respectively. Compared with the source optimization approach using genetic algorithm, the proposed approach leads to faster convergence while improving the image quality at the same time. The robustness of the proposed approach to initial sources is also verified.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.