InGaAs high electron mobility transistors （HEMTs） on InP substrate with very good device performance have been grown by mental organic chemical vapor deposition （MOCVD）. Room temperature Hall mobilities of the 2-DEG are measured to be over 8 700 cm^2/V-s with sheet carrier densities larger than 4.6× 10^12 cm^ 2. Transistors with 1.0 μm gate length exhibits transconductance up to 842 mS/ram. Excellent depletion-mode operation, with a threshold voltage of-0.3 V and IDss of 673 mA/mm, is realized. The non-alloyed ohmic contact special resistance is as low as 1.66×10^-8 Ω/cm^2, which is so far the lowest ohmic contact special resistance. The unity current gain cut off frequency （fT） and the maximum oscillation frequency （fmax） are 42.7 and 61.3 GHz, respectively. These results are very encouraging toward manufacturing InP-based HEMT by MOCVD.
Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.
