Modeling method of gallium nitride high-electron-mobility transistor large signal statistical model
A technology with high electron mobility and modeling methods, applied in the field of effective circuit statistical model modeling, can solve problems such as prone to outliers, non-convergence, and large data volume, and achieve simple modeling methods, avoiding difference values, and production The effect of stable quality
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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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