On Product Overlay (OPO) is a critical budget for advanced lithography. LithoInSight (LIS), an ASML application product, has proven to improve the ability of advanced process control (APC) for overlay with accurate fingerprint estimation and optimized scanner correction. It is now often used as Process of Record (PoR) for performing chuck/lot based run-to-run (R2R) control in a High Volume Manufacturing (HVM) environment. In order to further improve the on-product performance given the ever-tightening overlay spec. in advanced nodes, the question of how to reduce wafer-to-wafer process-induced variation has been asked frequently. Studies have shown that the wafer-to-wafer overlay variation is driven by certain critical process contexts. Aiming to bring a solution to the HVM phase, the ASML and Micron Data Science teams developed a Wafer Level Grouping Control (WLGC) methodology to perform overlay control given the process context information. This methodology has been implemented in one of the Micron production fabs, and demonstrated both reduced wafer-to-wafer (W2W) overlay variation and improved device yield on a yield-critical layer for a product from Micron 1z DRAM node.
In this proceeding, we use optical modeling and detailed balance analysis to predict the limiting efficiency of nanostructured silicon solar cells based on vertically-aligned nanowire and nanohole arrays. We first use the scattering matrix method to study broadband optical absorption. By incorporating the calculated optical absorption into a detailed balance analysis, we obtain the limiting short circuit current, open circuit voltage, and power conversion efficiency of nanowire and nanohole solar cells. Results show that optimized nanowire and nanohole arrays of 2.33 microns in height have 83% and 97% higher power conversion efficiencies than a thin film with the same height, respectively. Furthermore, we find that the limiting power conversion efficiency is mainly determined by the short circuit current density, which is proportional to the broadband optical absorption.
We report the design, fabrication, and optical absorption measurement of silicon membranes patterned with partially aperiodic nanohole structures. We demonstrate excellent agreement between measurement and simulations. We optimize a partially aperiodic structure using a random walk algorithm and demonstrate an experimental broadband absorption of 4.9 times that of a periodic array.
In this paper we formulate a novel AND/OR graph representation capable of describing the different configurations of deformable articulated objects such as horses. The representation makes use of the summarization principle so that lower level nodes in the graph only pass on summary statistics to the higher level nodes. The probability distributions are invariant to position, orientation, and scale. We develop a novel inference algorithm that combined a bottom-up process for proposing configurations for horses together with a top-down process for refining and validating these proposals. The strategy of surround suppression is applied to ensure that the inference time is polynomial in the size of input data. The algorithm was applied to the tasks of detecting, segmenting and parsing horses. We demonstrate that the algorithm is fast and comparable with the state of the art approaches.
In this paper, we use the transfer matrix method to calculate the optical absorptance of vertically-aligned silicon nanowire (SiNW) arrays. For fixed filling ratio, significant optical absorption enhancement occurs when the lattice constant is increased from 100 nm to 600 nm. The enhancement arises from an increase in field concentration within the nanowire as well as excitation of guided resonance modes. We quantify the absorption enhancement in terms of ultimate efficiency. Results show that an optimized SiNW array with lattice constant of 600 nm and wire diameter of 540 nm has a 72.4% higher ultimate efficiency than a Si thin film of equal thickness. The enhancement effect can be maintained over a large range of incidence angles.
The availability of massive earth observing satellite data provide huge opportunities for land use and land cover mapping. However, such mapping effort is challenging due to the existence of various land cover classes, noisy data, and the lack of proper labels. Also, each land cover class typically has its own unique temporal pattern and can be identified only during certain periods. In this article, we introduce a novel architecture that incorporates the UNet structure with Bidirectional LSTM and Attention mechanism to jointly exploit the spatial and temporal nature of satellite data and to better identify the unique temporal patterns of each land cover. We evaluate this method for mapping crops in multiple regions over the world. We compare our method with other state-of-the-art methods both quantitatively and qualitatively on two real-world datasets which involve multiple land cover classes. We also visualise the attention weights to study its effectiveness in mitigating noise and identifying discriminative time period.
We study the effect of aperiodicity on silicon nanorod antireflection structures. Numerical results reveal that randomness is beneficial for small nanorod sizes. A guided random walk algorithm is used to obtain optimized aperiodic structures.
We present a novel switched-capacitor, integrator-multiplexing, second-order delta-sigma modulator (DSM) featuring a single differential difference amplifier (DDA). Power consumption is low and resolution is high when this DSM is used for portable electroencephalographic applications. A single DDA (rather than a conventional operational transconductance amplifier) with appropriate switch and capacitor architectures is used to create the second-order switched-capacitor DSM. The configuration ensures that the resolution is high. The modulator was implemented using a standard 180 nm complementary metal–oxide–silicon process. At a supply voltage of 1.8 V, a signal bandwidth of 250 Hz and a sampling frequency of 200 kHz, simulations demonstrated that the modulator achieved an 82 dB peak signal-to-noise–distortion ratio and an effective number of bits of 14.
Language and image understanding are two major goals of artificial intelligence which can both be conceptually formulated in terms of parsing the input signal into a hierarchical representation. Natural language researchers have made great progress by exploiting the 1D structure of language to design efficient polynomial-time parsing algorithms. By contrast, the two-dimensional nature of images makes it much harder to design efficient image parsers and the form of the hierarchical representations is also unclear. Attempts to adapt representations and algorithms from natural language have only been partially successful. In this paper, we propose a Hierarchical Image Model (HIM) for 2D image parsing which outputs image segmentation and object recognition. This HIM is represented by recursive segmentation and recognition templates in multiple layers and has advantages for representation, inference, and learning. Firstly, the HIM has a coarse-to-fine representation which is capable of capturing long-range dependency and exploiting different levels of contextual information. Secondly, the structure of the HIM allows us to design a rapid inference algorithm, based on dynamic programming, which enables us to parse the image rapidly in polynomial time. Thirdly, we can learn the HIM efficiently in a discriminative manner from a labeled dataset. We demonstrate that HIM outperforms other state-of-the-art methods by evaluation on the challenging public MSRC image dataset. Finally, we sketch how the HIM architecture can be extended to model more complex image phenomena.
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