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Practice shows that in all sectors of the economy there are network educations of entrepreneurs that contribute to improving the competitiveness of small businesses in various sectors of the economy. The network model of business organization is focused on a stable set of independent economic entities involved in the creation of goods and services based on contracts that facilitate the coordination of activities. The purpose of the work is to study the activities of small businesses in the construction industry and identify state mechanisms for its support through the functioning of business networks. The authors prove that the development of the construction industry is one of the most effective ways of economic growth, and identify the main advantages of using business networks for small business development: providing resources, increasing labor productivity, and increasing opportunities for innovation. At the same time, the problems of functioning of business structures in the construction industry and the consequences of their activities in the shadow sector of the economy are shown. The scientific article reveals the role of small business in the construction industry as a driver of growth in the socio-economic development of the region, presents the stages of formation of business networks small business, identifies the main elements of the system of state support for small business within the organization of business networks.

Risk is an important factor that results from the execution of any economic decision and occurs in the conditions of uncertainty and cyclical development inherent in a market economy. Classification and assessment of risks specific to construction organizations is a complex and strategic task based on the application of various models, taking into account the industry’s risk management features. The purpose of this work is to study the methodological and practical aspects of risk assessment for making management decisions in the investment and construction sector. The study focuses on such interrelated concepts as “uncertainty” and “risk” and their impact on business performance in the market conditions. The authors prove that the risks typical for the construction industry in Russia have their own specific features and depend on such significant factors as the level of taxes, the volume of orders, the cost of materials and structures, the availability of sufficient financial resources, and the payment capabilities of customers. Depending on the categories of construction companies, the most significant risks are identified (technological, commercial, investment and innovation risks). The authors studied the essence and features of risk assessment taking into account international standards and practical aspects in the investment and construction complex. The scientific article reveals the role of risk management in risk management in the investment and construction sector, the main problems of using methods to minimize risks for various categories of construction organizations in Russian practice.

A novel angle-resolved scatterometer based on pupil optimization for feature profile measurement in a photolithography process is proposed. The impact of image sensor errors is minimized by optimizing the intensity distribution of the incident light using a spatial light modulator. The scatterometry sensitivity of feature measurement at different polarization conditions is calculated using the rigorous coupled-wave and first-order analyses, and the reproducibility of the scatterometer is evaluated. The results show that the sensitivity and reproducibility of the angle-resolved scatterometer increase by 90% and 40% with pupil optimization, respectively.

The impact of lens aberrations becomes severe when the critical dimensions (CDs) shrink. The accurate measurement of both low- and high-order Zernike aberrations is important during a photolithographic process. Based on the multi-illumination settings and principal component analysis of aerial images, a novel in situ aberration measurement technique that can accurately measure all the Zernike aberrations, except for the sinusoidal 2-\theta and sinusoidal 4-\theta terms (under polar coordinates, and Z1 to Z4 are not considered) is proposed in this letter. The estimated maximum error of the Zernike aberrations ranges from 0.43 to 0.78 m\lambda when the amplitudes of the Zernike coefficients range from –20 to 20 m\lambda. The standard and root mean square errors are both in the range from 0.14 to 0.4 m\lambda.

A bilinear photoresist model extends the approach of a convolution kernel-style process models to include second-order effects. Using effective acid concentration after post-exposure bake as the latent image we approximate the reactiondiffusion operator as a second order Volterra series. The series expansion is carried out for a calibrated photoresist model. Both the linear kernel and the Bilinear Resist Transfer Function (BRTF) are estimated at the same time using a set of pre-computed training images. The linear kernels of the Volterra series are found to have a Gaussian behavior, while the shape of BRTF appears to vary greatly depending on exact details of photoresist composition. Accuracy of the estimated photoresist operator is studied using process window matching at multiple sets of optical conditions. The estimated operator is applied to a set of validation aerial images and the resultant CD values are compared against full photoresist simulation. The bilinear model performance is found to be within 1 nm of the full photoresist model across the full range of dose and focus values.

Lithographic tool performance is the main contributor to CDU. The tool designers and users require an accurate method to measure the tool's error factors on the wafer side in order to improve CDU. Engineers typically use the FEM method to estimate DOF and EL, and then predict the CDU. However, based on the exposure data, it is often difficult to separate systematic level physical errors, such as DOSE repeatability, focus repeatability, dynamic errors and all the other tool's imperfections. In this paper, we introduce a wafer data based method to diagnose tool's performance for CDU improvement. As the systematic errors have a specific signature, they generate a fingerprint in the exposure data. Based on the knowledge of the exposure process and process flow, multiple dimensions exposure matrix is designed to analyze and diagnose the tool's systematic error from wafer data fingerprint. For SMEE's scanner tool (SSA600/10), we use this method to diagnose tool's systematic error and improve the CDU. Some typical result is represented in this paper.

A bilinear photoresist model is accurate, fast, and potentially reversible. Similar to other image-processing style (blur kernel) models, this model represents a transformation of an aerial image into a latent image. The key difference is the explicit recognition of the non-linearity of the process while retaining common signal processing architecture. By applying a Volterra series expansion to the reaction-diffusion functional, a high-accuracy representation of the process is obtained. Several methods for identifying the double-impulse response of the quadratic term of the series are discussed. Characterization is carried out based on the bi-harmonic signal sampling method of the Bilinear Transfer Function, the Fourier transform of the double-impulse spread function. Several photoresist systems are characterized, and strong quadratic behavior is observed for many. The resulting estimated BTF are presented, and their differences are discussed.