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The existing research on the rock burst risk of faults under different conditions is mostly carried out around the footwall mining or single fault form. The comparative research on the rock burst risk of hanging wall mining of different faults is seldom involved. In order to solve the above problems, the 12220 working face of Gengcun Coal Mine in Yima, Henan is taken as the research background. The rock burst risk of mining in hanging wall of normal and reverse faults is analyzed by means of theoretical analysis, numerical simulation and field monitoring. The mechanical model of mining in the hanging wall of normal and reverse faults is established. The mechanical condition of shear slip on the fault is obtained by analyzing the stress of fault rock. The results of the theoretical analysis show that the occurrence of shear slip is closely related to such factors as fault dip angle, internal friction angle of fault and fault surface force on rock block during hanging wall mining of normal and reverse faults. The closer the working face is to the fault, the greater the risk of shear slip. The numerical simulation of the mining process in the hanging wall of normal and reverse faults is carried out, and the normal stress, shear stress and slippage of the fault plane are analyzed. The results show that in the mining process of the working face, when the distance between the working face and the fault is less than 40 m, the risk of shear slip and rock burst increases gradually. When the distance from the fault is 10 m, the risk is the greatest. The most likely position for shear slip is the coal seam roof and coal seam of the fault plane. The influence degree of the coal seam floor is obviously less than that of the roof. The type of fault has a certain impact on the rock burst risk. The rock burst risk of reverse fault mining is higher than that of normal fault. Microseismic monitoring of rock burst risk is carried out on the 12220 working face. The results show that when the working face is less than 20 m from the fault, the microseismic events are frequent and the rock burst risk is high. The results are consistent with the numerical simulation results, which verifies the rationality of the numerical simulation analysis.

We present a new method for in situ monitoring of a grating profile during the development process to control the critical dimensions of surface-relief rectangular photoresist gratings on transparent substrates. A He-Ne laser of visible wavelength is employed as the monitoring light source. We determine the height of the grating ridges from the first minimum value of the diffraction intensity curve of the zeroth transmission order and then obtain the duty cycle of the grating from the diffraction intensity of the -1st transmission order. The effectiveness of our method has been demonstrated through the fabrication of rectangular photoresist gratings of 1200, 2200, and 3000 lines/mm on glass substrates. The experimental results are statistically compared with results extracted from scanning electron micrographs. Good agreement between the online, real-time measured results and the scanning electron microscopy results is obtained. [c] 2010 Optical Society of America OCIS codes: 050.1950, 120.1880, 120.4290, 220.4241.

We employed a grating profile measurement method, which is based on the combination of multiwave-length scatterometry and artificial neural network, to determine the critical dimensions of submicrometer-period photoresist gratings with wavy sidewall profiles. Six laser beams in three wavelengths and two orthogonal polarizations were adopted for the scatterometry measurement, and the incident angle of each beam was chosen following principles that we propose for achieving high sensitivity. We measured diffraction efficiencies of a large number of photoresist gratings made on glass substrates and high-reflectivity multilayer substrates coated with a chromium thin-film layer, and determined the grating groove parameters using a neural network model. The experimental results are statistically compared with results extracted from scanning electron micrographs. Good agreements between the indirect, neural network predicted results and the direct, scanning electron microscopy results are obtained. OCIS codes: 050.1950, 120.4290, 100.3190.