Abstract
Breakdown voltage (BV), on-state voltage (Von), static latch-up voltage (Vlu), static latch-up current density (Jlu), and threshold voltage (Vth), etc., are critical static characteristic parameters of an IGBT for researchers. Von and Vth can characterize the conduction capability of the device, while BV, Vlu, and Jlu can help designers analyze the safe operating area (SOA) of the device and its reliability. In this paper, we propose a multi-layer artificial neural network (ANN) framework to predict these characteristic parameters. The proposed scheme can accurately fit the relationship between structural parameters and static characteristic parameters. Given the structural parameters of the device, characteristic parameters can be generated accurately and efficiently. Compared with technology computer-aided design (TCAD) simulation, the average errors of our scheme for each characteristic parameter are within 8%, especially for BV and Vth, while the errors are controlled within 1%, and the evaluation speed is improved more than 107 times. In addition, since the prediction process is mathematically a matrix operation process, there is no convergence problem, which there is in a TCAD simulation.
Highlights
The insulated gate bipolar transistor (IGBT) is widely used in power electronics due to its superior performance [1,2]
Taking the V on prediction as an example, we can further analyze the fitting results of the trained artificial neural network (ANN) relating to the relationship between the structural parameters and characteristic parameters of the IGBT
We propose a multi-layer ANN predictive framework to predict multiple static characteristic parameters of an IGBT, such as breakdown voltage (BV), V on, V lu, Jlu, and V th
Summary
The insulated gate bipolar transistor (IGBT) is widely used in power electronics due to its superior performance [1,2]. Simulation tools are used to obtain these characteristic parameters of the device before experimental testing because of the advantage of low prediction errors [1,2,3,4]. This method may suffer from non-convergence when solving the semiconductor physical equations, decreasing the efficiency of obtaining the characteristic parameters. Most work is limited to providing only one characteristic parameter, such as the threshold voltage of a junctionless nanowire transistor [11] or the breakdown voltage of a lateral
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