Machine Learning based Optical Proximity Correction Techniques

The shrinking of the size of the advanced technological nodes brings up new challenges to the semiconductor manufacturing community.The optical proximity correction(OPC)is invented to reduce the errors of the lithographic process.The conventional OPC techniques rely on the empirical models and optimization methods of iterative type.Both the accuracy and computing speed of the existing OPC techniques need to be improved to fulfill the stringent requirement of the research and design for latest technological nodes.The emergence of machine learning technologies inspires novel OPC algorithms.More accurate forward simulation of the lithographic process and single turn optimization methods are enabled by the machine learning based OPC techniques.We discuss the latest progress made by the OPC community in the process simulation and optimization based on machine learning techniques.

Evaluation of patient visual comfort and repeatability of refractive values in non-presbyopic healthy eyes

AIMTo evaluate the intra-operator repeatability in healthy subjects using the WAM-5500 auto-kerato/refractometer and the iTrace aberrometer, to compare the refractive values and the subjective refraction obtained with both devices and to determine which of these three spherocylindrical corrections allows the subject to achieve the best visual comfort.METHODSForty-two non-presbyopic healthy eyes of 42 subjects were enrolled in this prospective study. Refractive values were compared, evaluating the repeatability, the relationship between the methods and the best visual comfort obtained.RESULTSSphere, cylinder and axis results showed good intraclass correlation coefficients (ICC); the highest ICC was obtained using the spherical refraction with the autorefractometer and the aberrometer, achieving levels of 0.999 and 0.998, respectively. The power vector (PV) was calculated for each refraction method, and the results indicated that there were no statistically significant differences between them (P>0.05). Direct comparison of PV measurements using the three methods showed that aberrometer refraction gave the highest values, followed by the subjective values; the autorefractometer gave the lowest values. The subjective method correction was most frequently chosen as the first selection. Equal values were found for the autorefractometer and the aberrometer as the second selection.CONCLUSIONThe iTrace aberrometer and the WAM-5500 auto-kerato/refractometer showed high levels of repeatability in healthy eyes. Refractive corrections with the aberrometer, the autorefractometer and subjective methods presented similar results, but spherocylindrical subjective correction was the most frequently selected option. These technologies can be used as complements in refractive evaluation, but they should not replace subjective refraction.

A sparse matrix model-based optical proximity correction algorithm with model-based mapping between segments and control sites

Optical proximity correction (OPC) is a key step in modern integrated circuit (IC) manufacturing.The quality of model-based OPC (MB-OPC) is directly determined by segment offsets after OPC processing.However,in conventional MB-OPC,the intensity of a control site is adjusted only by the movement of its corresponding segment;this scheme is no longer accurate enough as the lithography process advances.On the other hand,matrix MB-OPC is too time-consuming to become practical.In this paper,we propose a new sparse matrix MB-OPC algorithm with model-based mapping between segments and control sites.We put forward the concept of 'sensitive area'.When the Jacobian matrix used in the matrix MB-OPC is evaluated,only the elements that correspond to the segments in the sensitive area of every control site need to be calculated,while the others can be set to 0.The new algorithm can effectively improve the sparsity of the Jacobian matrix,and hence reduce the computations.Both theoretical analysis and experiments show that the sparse matrix MB-OPC with model-based mapping is more accurate than conventional MB-OPC,and much faster than matrix MB-OPC while maintaining high accuracy.

Human eye ocular component analysis for refractive state and refractive surgery

AIM： To analyze the clinical factors influencing the human vision corrections via the changing of ocular components of human eye in various applications; and to analyze refractive state via a new effective axial length.METHODS： An effective eye model was introduced by the ocular components of human eye including refractive indexes, surface radius（r1, r2, R1, R2） and thickness（t, T） of the cornea and lens, the anterior chamber depth（S1） and the vitreous length（S2）. Gaussian optics was used to calculate the change rate of refractive error per unit amount of ocular components of a human eye（the rate function M）. A new criterion of myopia was presented via an effective axial length.RESULTS： For typical corneal and lens power of 42 and 21.9 diopters, the rate function Mj（j=1 to 6） were calculated for a 1% change of r1, r2, R1, R2, t, T（in diopters） M1=＋0.485, M2=-0.063, M3=＋0.053, M4=＋0.091, M5=＋0.012, and M6=-0.021 diopters. For 1.0 mm increase of S1 and S2, the rate functions were M7=＋1.35, and M8=-2.67 diopter/mm, respectively. These rate functions were used to analyze the clinical outcomes in various applications including laser in situ keratomileusis surgery, corneal cross linking procedure, femtosecond laser surgery and scleral ablation for accommodation.CONCLUSION： Using Gaussian optics, analytic formulas are presented for the change of refractive power due to various ocular parameter changes. These formulas provide useful clinical guidance in refractive surgery and other related procedures.

Study of Inverse Lithography Approaches based on Deep Learning

Computational lithography(CL)has become an indispensable technology to improve imaging resolution and fidelity of deep sub-wavelength lithography.The state-of-the-art CL approaches are capable of optimizing pixel-based mask patterns to effectively improve the degrees of optimization freedom.However,as the growth of data volume of photomask layouts,computational complexity has become a challenging problem that prohibits the applications of advanced CL algorithms.In the past,a number of innovative methods have been developed to improve the computational efficiency of CL algorithms,such as machine learning and deep learning methods.Based on the brief introduction of optical lithography,this paper reviews some recent advances of fast CL approaches based on deep learning.At the end,this paper briefly discusses some potential developments in future work.

Atmospheric correction of HJ-1 A/B CCD over land： Land surface reflectance calculation for geographical information product

This paper proposed a method to retrieve the land surface reflectance from the HJ-1A/B CCD data. The aerosol optical depth（AOD）, the most important factor affecting the atmospheric correction of CCD images at all bands, is proposed to retrieve from the CCD imagery by the approach of dense dark vegetation（DDV） method. A look-up table in terms of the transmittances, the path radiances and the atmospheric spherical albedo as functions of the AOD was established for a variety of sun-sensor geometry and aerosol loadings. The atmospheric correction is then achieved with the look-up table and the MODIS surface reflectance output（MOD09） as the priori datasets. Based on the retrieved AOD and the look-up table of atmospheric correction coefficients, the land surface reflectance was retrieved for the HJ-1A/B data according to the atmospheric radiative transfer equation. Some in-situ measurement Data for Yanzhou of Shandong province in East China and MODIS land surface reflectance products MOD09 are used to preliminarily validate the proposed method. The results show that the proposed method can remove effectively the atmospheric contributions, and the overall accuracy of the retrieval land surface reflectance can be improved substantially.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsuffcient precondition of the downstream in a new notion of Space Economy 4.0-Part 1:Problem background in Artificial General Intelligence(AGI)

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system.To overcome their drawbacks,an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsufficient precondition of the downstream in a new notion of Space Economy 4.0-Part 2:Software developments

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in addition to proven suitability.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.

A method of 3D reconstruction based on panoramic cameras

In this paper,a method of 3D reconstruction from two images acquired by two panoramic cameras is presented.Firstly,the features of the reconstruction object detected in each image are matched through the DP matching method.Secondly,optical correction is carried out on two cameras,and the internal parameters of panoramic cameras can be calculated.Finally,according to the calibration method,the geometric relationship between corresponding points in space and in two panoramic images is deduced.The results indicate that the method of 3D reconstruction based on two panoramic cameras is simple,and the accuracy can reach 98.82%.

Calculation and Analysis of Sulfide Capacities for CaO-Al_2O_3-SiO_2-MgO-TiO_2 Slags

The empirical models of sulfide capacity calculated by traditional optical basicity do not consider the charge compensation of alkaline metal ions to AP＋ in the molten slags, so that the deviations between the calculated values and measured ones of sulfide capacity are inevitable. The relation between sulfide capacity and the corrected optical basicity put forward by Mills considering the charge compensation was investigated. Combined with the relation be- tween sulfide capacity and temperatures, a novel and accurate calculation model of sulfide capacity was proposed, which was applied to calculate the sulfide capacities of CaO-Al2 O3-SiO2-MgO and CaO-Al2 03-SiO2-MgO-TiO2 sys-tems, where the sum of the CaO and MgO concentrations in the slags must be not lower than the Al2O3 concentra tion. It was also found that the calculated values were in a good agreement with the measured values, and the mean deviations were 2.57% and 2.65%, respectively.

SVM based layout retargeting for fast and regularized inverse lithography

Inverse lithography technology(ILT),also known as pixel-based optical proximity correction(PB-OPC),has shown promising capability in pushing the current 193 nm lithography to its limit.By treating the mask optimization process as an inverse problem in lithography,ILT provides a more complete exploration of the solution space and better pattern fidelity than the traditional edge-based OPC.However,the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process.To address this issue,in this paper we propose a support vector machine(SVM)based layout retargeting method for ILT,which is designed to generate a good initial input mask for the optimization process and promote the convergence speed.Supervised by optimized masks of training layouts generated by conventional ILT,SVM models are learned and used to predict the initial pixel values in the‘undefined areas’of the new layout.By this process,an initial input mask close to the final optimized mask of the new layout is generated,which reduces iterations needed in the following optimization process.Manufacturability is another critical issue in ILT;however,the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models.To compensate for this drawback,a spatial filter is employed to regularize the retargeted mask for complexity reduction.We implemented our layout retargeting method with a regularized level-set based ILT(LSB-ILT)algorithm under partially coherent illumination conditions.Experimental results show that with an initial input mask generated by our layout retargeting method,the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8%and 69.0%,respectively.

A novel OPC method to reduce mask volume with yield-aware dissection

Growing data volume of masks tremendously increases manufacture cost. The cost increase is partially due to the complicated optical proximity corrections applied on mask design. In this paper, a yield-aware dissec- tion method is presented. Based on the recognition of yield related mask context, the dissection result provides sufficient degrees of freedom to keep fidelity on critical sites while still retaining the frugality of modified designs. Experiments show that the final mask volume using the new method is reduced to about 50% of the conventional method.