The silicon-on-insulator (SOI) lateral doublediffused metal-oxide-semiconductor (LDMOS) with high-k multi-fingers (HKMFs) is proposed and investigated. The fingertips are distributed at specific locations to modulate the electric field distributions and improve the device performances. First, the electric field peaks formed at the fingertips could optimize the electric field distributions, which improves the breakdown voltage (BV) of the LDMOS effectively. Meanwhile, the multi-fingers are embedded into the drift region to increase the optimal drift doping concentration, which facilitates the positive conduction of the device and reduces the specific ON-resistance (R on,sp ). The simulation results show that the proposed HKMF-LDMOS with five multi-fingers increases the BV by 59.2%, reduces R on,sp by 37.8%, and improves the figure of merit (FOM) by 4.07 times when compared to the conventional LDMOS.
Due to the complexity of the 2D coupling effects in AlGaN/GaN HEMTs, the characterization of a device’s off-state performance remains the main obstacle to exploring the device’s breakdown characteristics. To predict the off-state performance of AlGaN/GaN HEMTs with efficiency and veracity, an artificial neural network-based methodology is proposed in this paper. Given the structure parameters, the off-state current–voltage (I–V) curve can therefore be obtained along with the essential performance index, such as breakdown voltage (BV) and saturation leakage current, without any physics domain requirement. The trained neural network is verified by the good agreement between predictions and simulated data. The proposed tool can achieve a low average error of the off-state I–V curve prediction (Ave. Error < 5%) and consumes less than 0.001‰ of average computing time than in TCAD simulation. Meanwhile, the convergence issue of TCAD simulation is avoided using the proposed method.
The breakdown performance of the AlGaN/GaN high electron mobility transistor (HEMT) is limited by the high electric field peaks in the device. To obtain a more uniform electric field distribution, the drift region width modulation (DWM) technique is proposed to reshape the charge distribution between the gate and drain electrodes. By applying the Gaussian box method, an effective designing guidance for the structure optimization of the AlGaN/GaN HEMT with adaptive-width drift region pillars (AWD-HEMT) is obtained. The fabricated AWD-HEMT demonstrated a significant improvement in breakdown performance. The Baliga's figure of merit (BFOM) of AWD-HEMT improved 65.3% compared with the conventional HEMT, without introducing complicated processes. In addition, the mechanism of the AWD-HEMT is explored by numerical methods. The simulations indicate that a more uniform electric field distribution at AlGaN/GaN interfaces could be obtained, and the increased varying rate of the adaptive-width drift region (AWD) pillars results in a more obvious electric field modulation effect.
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