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The machining of ultra-smooth surfaces has always been a significant research topic in the fields of optics fabrication. In this paper, disc hydrodynamic polishing (DHDP) process with a novel polishing tool is proposed to produce ultra-smooth surface with sub-nanometer scale surface roughness (Ra

Nowadays, the fabrication of ultra-smooth surface play critical roles in, integrated circuits (IC) manufacturing, especially for semiconductor substrate materials of silicon, silicon dioxide and silicon carbide wafer. To efficiently production ultra-smooth semiconductor wafer surface, a novel Disc Hydrodynamic Polishing (DHDP) process is proposed. In DHDP process, the polishing slurry was injected from the center of polishing tool and a screw pressure groove was designed on the polishing pad. Then, the fluid film will be formed between the disc polishing pad and the wafer surface, under the high rotation speed and applied load of disc polishing tool. In the fluid film, the particles were droved by the hydrodynamic pressure and shear stress impact the wafer workpiece. The abrasive particles erode the workpiece under the hydrodynamic pressure in the fluid film. Then, the effect of tool speed and machining time on the quality of surface is studied, through a series of experiments. The rotational speed of polishing tool can improve the surface quality. The surface of fused quartz glass wafer surface roughness(Ra) could reach 0.7nm and the peak value(PV) is 8.9nm, which was polished by DHDP process. Moreover, DHDP process can efficiently reduce the surface roughness and acquire ultra-smooth surface for semiconductor wafer.

In order to calibrate the high-order coefficients and to increase the experimental accuracy of accelerometers tested on centrifuge, the D-optimal theory is applied in the experimental optimal design. Due to that the low-order items of accelerometers are inaccurate in the test on centrifuge, the truncated D-optimal algorithm is presented to solve this problem. The iterative computational algorithm is repeated until the maximal dispersion points of model equations are achieved. The experimental results demonstrate that the proposed method used in model identification of high-order parameters outperforms general D-optimal design methods.

The error model is nonlinear when the azimuth angle of strapdown inertial navigation system (SINS) on stable base is large, and a new filter results from using unscented Kalman filter for proposal distribution generation imbedding latest observed measurements in importance sampling step, and combining Gaussian mixture model and weighted expectation maximization (EM) algorithm to replace the traditional resampling step. And the "sample depletion" problem was lessened. It is demonstrated by simulation that this new approach has an improved estimation performance in initial alignment of large azimuth misalignment on static base of SINS.

Model identification of accelerometer is one of the most pervasive problems in calibration test of accelerometer. A method of calibrating dual orthogonal accelerometers on two-axis table is presented to eliminate the effect of the angle-setting error on calibration, thus enhancing identification precision of the accelerometer. The correlation of the model coefficients of dual orthogonal accelerometers is analyzed and the D-optimal experiment design is accomplished. Furthermore analytical solutions are deduced. The simulation experiment and measured data suggests that the standard deviation of identification of dual orthogonal accelerometers is one order of magnitude less than that of single accelerometer. Therefore, the precision of identifying the error model of accelerometer is improved.