Search

Neuroinflammation plays an early and prominent role in the pathology of Alzheimer's disease (AD). Tumor necrosis factor-α-induced protein 8-like 2 (TIPE2) has been identified as a negative regulator of innate and adaptive immunity. However, whether TIPE2 affects cognitive functions in AD-like mouse models remains unknown. In this study, we compared the gene and protein expressions of TIPE2 between the APP/PS1 mice and the age-matched wild type (WT) mice at different stages of development using western blot and RT-qPCR. The hippocampal expression of the TIPE2 mRNA and protein in APP/PS1 mice was higher than that of the WT mice starting from 6 months to 10 months. However, the difference of the TIPE2 expression between the APP/PS1 mice and the WT mice declined in a time-dependent manner. The spatial learning and memory deficit from the 8-month-old APP/PS1 mice was observed in the Y-maze test and fear conditioning task. Interestingly, overexpression of TIPE2 by intra-hippocampal injection of AAV-TIPE2 into APP/PS1 mice resulted in an improvement of learning and memory and reduced expression of inflammatory cytokines, such as TNF-α, IL-6 and IL-1β, and increased expression of anti-inflammatory cytokines, such as IL-10 and Arg-1. Taken together, our findings show that the TIPE2 expression level was negatively correlated with the pathogenesis of Alzheimer's disease, and overexpression of TIPE2 attenuates cognitive deficits in APP/PS1 mice, suggesting TIPE2 is a potential target for pharmacological intervention and improvement of cognitive deficits. Graphical Abstract .

Using a low-field magnetic resonance scanner, we have obtained images of gaseous polarized 129Xe and water cells at room temperature. This potentially low-cost imaging technique offers the possibility of high-resolution imaging using both polarized noble gas and proton magnetic resonance imaging of tissues in the same scanner.

IC manufacturers have a strong demand for transferring a working process from one scanner to another. Recently, a programmable illuminator (FlexRayTM) became available on ASML ArF immersion scanners that, besides all the parameterized source shapes of the earlier AerialTM illuminator (based on diffractive optical elements) can also produce any desired freeform source shape. As a consequence, a fabrication environment may have scanners with each of the illuminator types so both FlexRay-to-Aerial and FlexRay-to-FlexRay matching is of interest. Moreover, the FlexRay illuminator itself is interesting from a matching point-of-view, as numerous degrees of freedom are added to the matching tuning space. This paper demonstrates how the upgrade of an exposure tool from Aerial to FlexRay illuminator shows identical proximity behavior without any need for scanner tuning. Also, an assessment of the imaging correspondence between exposure tools each equipped with a FlexRay illuminator is made. Finally, for a series of use-cases where proximity differences do exist, the application of FlexRay source tuning is demonstrated. It shows an enhancement of the scanner matching capabilities, because FlexRay source tuning enables matching where traditional NA and sigma tuning are shortcoming. Moreover, it enables tuning of freeform sources where sigma tuning is not relevant. Pattern MatcherTM software of ASML Brion is demonstrated for the calculation of the optimized FlexRay tuned sources.

With shrinking feature sizes and error budgets in OPC models, effective pattern coverage and accurate measurement become more and more challenging. The goal of pattern selection is to maximize the efficiency of gauges used in model calibration. By optimizing sample plan for model calibration, we can reduce the metrology requirement and modeling turn-around time, without sacrificing the model accuracy and stability. With the Tachyon pattern-selection-tool, we seek to parameterize the patterns, by assessing dominant characteristics of the surroundings of the point of interest. This allows us to represent each pattern with one vector in a finite-dimensional space, and the entire patterns pool with a set of vectors. A reduced but representative set of patterns can then be automatically selected from the original full set sample data, based on certain coverage criteria. In this paper, we prove that the model built with 56% reduced wafer data could achieve comparable quality as the model built with full set data. Keyword: OPC, model calibration, test pattern coverage, pattern selection

FlexRay programmable illumination and LithoTuner software is combined in several use cases. The first use case is optical proximity error (OPE) minimization. Simulation predicts the rms OPE error is reduced by 39% with LithoTuner and FlexRay, and is confirmed via experiment with a reduction of 33%. For minimizing the OPE error, two types of illumination tuning was performed, sigma tuning and freeform tuning. The sigma tuning is able to reduce the mean-totarget critical dimension (CD) error, but the CD error variance is unaffected. Freeform tuning, however, is able to reduce both the mean-to-target CD and the CD error variance. The second use case is matching two ArF scanners, a XT:1950Hi with FlexRay to a XT:1700Fi with diffractive optical element (DOE) illumination. With LithoTuner and FlexRay, simulation predicts the CD error post-matching is reduced by 51%, and experiment was able to achieve a reduction of 29%.

Proximity matching is a common activity in the wafer fabs 1,2,3 for purposes such as process transfer, capacity expansion, improved scanner yield and fab productivity. The requirements on matching accuracy also become more and more stringent as CD error budget shrinks with the feature size as technology advances. Various studies have been carried out, using scanner knobs including NA, inner sigma, outer sigma, stage tilt, ellipticity, and dose. In this paper, we present matching results for critical features of a logic device, between an ASML XT:19x0i scanner and an XT:1700i (reference), demonstrating the advantage of freeform illuminator pupil as part of the adjustable knobs to provide additional flexibility. We also present the investigation of a novel method using lens manipulators for proximity matching, effectively injecting scalar wavefront to an XT:19x0i to mimic the behavior of the XT:1700i lens.

Given the continually decreasing k1 factor and process latitude in advanced technology nodes, it is important to fully understand and control the variables that impact imaging behavior in the lithography process. In this joint work between TSMC and ASML, we use model-based simulations to characterize and predict the imaging effects of these variables and to fine-tune the scanner settings based on such information in order to achieve optimal printing results on a perreticle basis. The scanner modeling makes use of detailed scanner characteristics as well as wafer CD measurements for accurate model construction. Simulations based on the calibrated model are subsequently used to predict the wafer impact of changes in tunable scanner parameters for all critical patterns in the product. The critical patterns can be identified beforehand, either experimentally on wafer, mask or through model simulations. A set of optimized scanner setting offsets, known as a "scanner tuning recipe" is generated to improve the imaging behavior for the critical patterns. We have demonstrated the efficacy of this methodology for multiple-use cases with selected ASML scanners and TSMC processes and will share the achieved improvements on defect reduction and yield improvements.

Scanner matching based on wafer data has proven to be successful in the past years, but its adoption into production has been hampered by the significant time and cost overhead involved in obtaining large amounts of statistically precise wafer CD data. In this work, we explore the possibility of optical model based scanner matching that maximizes the use of scanner metrology and design data and minimizes the reliance on wafer CD metrology. A case study was conducted to match an ASML ArF immersion scanner to an ArF dry scanner for a 6Xnm technology node. We used the traditional, resist model based matching method calibrated with extensive wafer CD measurements and derived a baseline scanner manipulator adjustment recipe. We then compared this baseline scanner-matching recipe to two other recipes that were obtained from the new, optical model based matching method. In the following sections, we describe the implementation of both methods, provide their predicted and actual improvements after matching, and compare the ratio of performance to the workload of the methods. The paper concludes with a set of recommendations on the relative merits of each method for a variety of use cases.

Given the decrease in k1 factor for 65nm-node lithography technology and beyond, it is increasingly important to understand and control the variables which impact scanner imaging behavior in the lithography process. In this work, we explore using model simulations to characterize and predict imaging effects of these variables, and then based on such information to fine-tune the scanner settings to obtain printing results optimally matched to a reference scanner. The scanner modeling makes use of detailed scanner characteristics as well as wafer CD measurements for accurate model construction. To identify critically mismatched patterns on a production layout, we employ the fast full-chip simulation capability provided by Brion’s Tachyon servers. Tachyon simulations are also used to predict wafer impacts of changes in tunable scanner parameters. A set of optimized scanner variable offsets, called a “scanner tuning recipe”, is generated to minimize overall imaging mismatch between two scanners. As a proof-of-concept, we have carried out scanner tuning procedures on selected ASML scanners. The results show improvements more than 20% on CD offset RMS values for 2D line-end patterns, production layout patterns, and the mismatched patterns identified with the full-chip simulation. Improvements on wafer-acceptance-test results and production yield on the to-be-tuned scanner are also observed.

The challenge for the upcoming full-chip CD uniformity (CDU) control at 32nm and 22nm nodes is unprecedented with expected specifications never before attempted in semiconductor manufacturing. To achieve these requirements, OPC models not only must be accurate for full-chip process window characterization for fine-tuning and matching of the existing processes and exposure tools, but also be trust-worthy and predictive to enable processes to be developed in advance of next-generation photomasks, exposure tools, and resists. This new OPC requirement extends beyond the intended application scope for behavior-lumped models. Instead, separable OPC models are better suited, such that each model stage represents the physics and chemistry more completely in order to maintain reliable prediction accuracy. The resist, imaging tool, and mask models must each stand independently, allowing existing resist and mask models to be combined with new optics models based on exposure settings other than the one calibrated previously. In this paper, we assess multiple sets of experimental data that demonstrate the ability of the Tachyon TM FEM (focus and exposure modeling) to separate the modeling of mask, optics, and resists. We examine the predictability improvements of using 3D mask models to replace thin mask model and the use of measured illumination source versus top-hat types. Our experimental wafer printing results show that OPC models calibrated in FEM to one optical setting can be extrapolated to different optical settings, with prediction accuracy commensurate with the calibration accuracy. We see up to 45% improvement with the measured illumination source, and up to 30% improvement with 3D mask. Additionally, we observe evidence of thin mask resist models that are compensating for 3D mask effect in our wafer data by as much as 60%.

To minimize or eliminate lithography errors associated with optical proximity correction, integrated circuit manufacturers need an accurate, predictive, full-chip lithography model which can account for the entire process window (PW). We have validated the predictive power of a novel focus-exposure modeling methodology with wafer data collected across the process window at multiple customer sites. Tachyon Focus-Exposure Modeling (FEM) first-principle, physics-driven simulations deliver accurate and predictive full-chip lithography modeling for producing state-of-the-art circuits.

By measuring the relaxation rates of the nuclear spin polarization of Xe-129 in the presence of alkali-metal vapor at different densities, we have extracted the spin-exchange rates between Xe-129 and the three alkali metals K, Rb, and Cs. By studying the alkali-metal-Xe-129 spin-exchange rates as functions of the cell number density from 0.2 to 0.7 amagat, the binary collision and van der Waals molecular terms are separated, and constants governing both mechanisms are determined. The results from our work can be used to optimize the parameter space for polarizing Xe-129, a promising agent for magnetic resonance imaging and other applications.

We report on measurements of the polarization for 3 He using optically pumped potassium vapor. We achieve a 3 He polarization greater than 40% in a mid-sized double-chamber glass target cell filled with 3 He gas using a single Ti:sapphire laser. Applied to the production of polarized 3 He as an atomic beam source for colliders, these developments could have significant impact on future studies of neutron spin structure. The results also motivate the development of high power diode lasers tuned to the appropriate potassium wavelengths.

FlexRay programmable illumination and LithoTuner software is combined in several use cases. The first use case is source mask optimization (SMO) in which the process window is maximized for a static random access memory (SRAM) design. In a 55 nm half-pitch contact hole array (k 1 = 0.38, NA= 1.35, λ= 193 nm), the process window (PW) with FlexRay programmable illumination is twice as large as the PW with cQuad illumination defined through completely refractive illumination. The second use case is optical proximity error (OPE) minimization in which the large PW from SMO is realized on every scanner in the fab. The OPE error is reduced by 17% with LithoTuner and FlexRay. The third use case is matching two ArF scanners, a 1950i with FlexRay to a 1700i with diffractive optical element (DOE) illumination. With LithoTuner and FlexRay, the root means squared (rms) critical dimension (CD) error is reduced by 29% in this third use case. The last use case, intrafield dose optimization with DoseMapper is combined with FlexRay programmable source optimization to reduce the CD error on a wafer from a mean-to-target critical dimension (CD) error in the mask manufacturing process. This combination was optimized with LithoTuner to reduce the root mean square (rms) wafer CD error by 30%.