• 한국정보통신학회논문지
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• 제7권4호
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• pp.743-749
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• 2003

본 논문에서는, cold-HEMT의 게이트에 핀치오프 전압보다 약간 낮은 전압을 가함으로써 게이트 손상문제로부터 자유로우며 부가적인 DC 측정을 필요로 하지 않는 새로운 HEMT 소신호 모델링 방법을 제시한다. 제시된 방법에 의해서 모델링된 회로의 S-파라미터 이론치는 49개의 동작 바이어스점에서 측정치와 62GHz까지 뛰어난 일치를 보였다.

• 한국전자통신학회논문지
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• 제9권8호
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• pp.853-859
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• 2014

본 연구에서는 핀치오프 된 cold-FET에서 게이트와 드레인 패드를 디임베딩하여 얻어지는 Z-파라미터와 게이트와 드레인 패드 커패시턴스를 제외한 핀치오프 된 cold-FET의 나머지 파라미터에 의해 모델링되는 Z-파라미터 사이의 잔차 오차를 최소화함으로써 HEMT의 모든 외인성 파라미터를 추출하는 기법을 제시한다. 제시된 기법을 사용하면 게이트와 드레인 모조패드의 추가적 제작 없이 게이트와 드레인 패드의 커패시턴스 값뿐 아니라 나머지 외인성 파라미터 값 모두를 성공적으로 추출할 수 있다.

• JSTS:Journal of Semiconductor Technology and Science
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• 제2권1호
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• pp.59-69
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• 2002

A semi-analytical model based on exact numerical analysis of the 2DEG channel in pseudo-morphic HEMT (PMHEMT) is presented. The exactness of the model stems from solving both Schrodinger's wave equation and Poisson's equation simultaneously and self-consistently. The analytical modeling of the device terminal characteristics in relation to the charge control model has allowed a best fit with the geometrical and structural parameters of the device. The numerically obtained data for the charge control of the channel are best fitted to analytical expressions which render the problem analytical. The obtained good agreement between experimental and modeled current/voltage characteristics and small signal parameters has confirmed the validity of the model over a wide range of biasing voltages. The model has been used to compare both the performance and characteristics of a PMHEMT with a competetive HEMT. The comparison between the two devices has been made in terms of 2DEG density, transfer characteristics, transconductance, gate capacitance and unity current gain cut-off frequency. The results show that PMHEMT outperforms the conventional HEMT in all considered parameters.

• JSTS:Journal of Semiconductor Technology and Science
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• 제6권3호
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• pp.182-188
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• 2006

An artificial neural network model for the microwave characteristics of an InGaAs/InP hemt for 70 nm gate length has been developed. The small-signal microwave parameters have been evaluated to determine the transconductance and drain-conductance. We have further investigated the frequency characteristics of the device. The neural network training have been done using the three layer architecture using Levenberg-Marqaurdt Backpropagation algorithm. The results have been compared with the experimental data, which shows a close agreement and the validity of our proposed model.