Large signal statistical model modeling method for gallium nitride high electron mobility transistor

A technology with high electron mobility and modeling methods, applied in the field of effective circuit statistical model modeling, can solve problems such as non-convergence, large amount of data, and easy occurrence of abnormal values, and achieve avoidance of difference values, stable production quality, and modeling The effect of simple method

Inactive Publication Date: 2016-08-10
徐跃杭 +1
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Problems solved by technology

However, this method uses the Monte Carlo method to statistically analyze the parameters of the large-signal equivalent circuit. Due to the large amount of data in this method, outliers are prone to appear, which will lead to non-convergence problems.

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  • Large signal statistical model modeling method for gallium nitride high electron mobility transistor
  • Large signal statistical model modeling method for gallium nitride high electron mobility transistor
  • Large signal statistical model modeling method for gallium nitride high electron mobility transistor

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Embodiment 1

[0038] Embodiment 1: as figure 1 Said, the present invention provides a GaN high electron mobility transistor large signal statistical model modeling method, including:

[0039] Step 101: testing several GaN high electron mobility transistors in each batch to obtain the current-voltage characteristics of the GaN high electron mobility transistors;

[0040] Step 102: using the current-voltage characteristic for the large-signal equivalent circuit model of the gallium nitride high electron mobility transistor, and extracting the large signal equivalent circuit parameters of the gallium nitride high electron mobility transistor;

[0041] Step 103: According to the parameters of the large-signal equivalent circuit, a large-signal statistical model is established by using the response curve method.

[0042] The statistical model modeling method provided in this embodiment obtains the IV characteristics of multiple gallium nitride high electron mobility transistors by testing sever...

Embodiment 2

[0049] Embodiment 2: as figure 2 As shown, as an optional implementation, the method of using the response curve method to establish a large signal statistical model includes:

[0050] Step 201: Select several sensitive parameters among the large-signal equivalent circuit parameters, and define the largest sensitive parameter among the sensitive parameters as X H , the smallest sensitive parameter is X L , other sensitive parameters are intermediate sensitive parameters X, and the calculation formula of intermediate sensitive parameters X is: X=b×C+a, where a=(X H +X L ) / 2, b=(X H -X L ) / 2, the variation range of variable C is -1~1;

[0051] Step 202: Selecting three states where the value of each sensitive parameter changes -10%, 0 and 10%, and combining the three states of several sensitive parameters to obtain N sets of simulation parameters;

[0052] Step 203: Substituting the simulation parameters into the ADS software for simulation to obtain a large signal statis...

Embodiment 3

[0054] Embodiment 3: As an optional implementation manner, after the large signal statistical model is established by using the response curve method, it further includes: verifying the accuracy of the large signal statistical model.

[0055] In this embodiment, the method for verifying the accuracy of the large signal statistical model includes:

[0056] Step 301: Acquiring the large signal characteristics of GaN high electron mobility transistors;

[0057] Step 302: selecting several sensitive parameters of the large-signal equivalent circuit parameters and substituting them into the large-signal statistical model to obtain a large-signal simulation result;

[0058] Step 303: Compare the large-signal simulation results with the large-signal characteristics.

[0059] In this embodiment, the large signal simulation results include simulated output power, simulated power added efficiency, and simulated gain. Large signal characteristics include output power Pout, power added ...

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Abstract

The invention provides a large signal statistical model modeling method for a gallium nitride high electron mobility transistor. The method comprises the following steps: testing a plurality of gallium nitride high electron mobility transistors in each batch, and acquiring current-voltage characteristics of the gallium nitride high electron mobility transistors; allowing the current-voltage characteristics to be used in large signal equivalent circuit models of the gallium nitride high electron mobility transistors, and extracting large signal equivalent circuit parameters about the gallium nitride high electron mobility transistors; and using a response curve method to build a large signal statistical model according to the large signal equivalent circuit parameters. The modeling method provided by the invention can reduce the amount of data, and avoid the problem of non-convergence when an abnormal value occurs, so that the statistical model obtained through the modeling method can accurately reflect process change conditions of different devices, and the production quality of the transistor is stable and balanced.

Description

technical field [0001] The invention belongs to the field of power devices, in particular to a large-signal equivalent circuit statistical model modeling method based on gallium nitride high electron mobility transistors. Background technique [0002] Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT, High Electron Mobility Transistor) is widely used in microwave circuits due to its high frequency, high power and other characteristics. Due to the influence of uncertainties such as process fluctuations, a statistical model is needed to analyze the circuit performance in the GaN monolithic circuit design process. Therefore, an accurate large-signal statistical model is an important part of the device modeling process. [0003] Due to the large contact resistance of GaNHEMT devices, the large-signal statistical models of first-generation semiconductor (silicon) and second-generation semiconductor (GaAs, InP, etc.) devices cannot be directly applied to GaNHEMT device...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/367G06F30/39G06F30/398
Inventor 徐跃杭陈志凯徐锐敏延波
Owner 徐跃杭
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