Top

Journal of Computational Electronics

Published in:

26-02-2024

Characteristics prediction and optimization of InP HBT using machine learning

Authors: Xiao Jie, Jie Wang, Xinjian Ouyang, Yuan Zhuang, Zhilong Wang, Shuzhen You, Dawei Wang, Zhiping Yu

Published in: Journal of Computational Electronics | Issue 2/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This study introduces a novel application of machine learning using indium phosphide heterojunction bipolar transistors as an example. The objective is to predict the device performance and optimize the device structure by utilizing an artificial neural network (ANN) to calculate the device direct current (DC) and frequency characteristics. To this end, we develop a physics-inspired ANN that emphasizes the significance of the first-order partial derivative of the current over voltage. The ANN is trained on a data set generated by technology computer-aided design simulations, covering a range of voltage setups, device geometries, and doping concentrations. The resulting model accurately predicts the DC and frequency characteristics of the device, and obtain key performance indicators such as the DC current amplification factor, cut-off frequency, and maximum oscillation frequency. This approach can significantly speed up the device parameter optimization and provide a potential numerical tool for design technology co-optimization.

previous article Novel analytical STFT expressions for nonlinear power engineering problem solving

next article Simulation of avalanche time in thin GaN/4H–SiC heterojunction avalanche photodiodes

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Ma, T., Moroz, V., Borges, R., Smith, L.: TCAD: present state and future challenges. In: 2010 International Electron Devices Meeting (IEDM), pp. 15–311534 (2010). https://doi.org/10.1109/IEDM.2010.5703367

Myung, S., Jang, W., Jin, S., Choe, J.M., Jeong, C., Kim, D.S.: Restructuring TCAD system: teaching traditional TCAD new tricks. In: 2021 IEEE International Electron Devices Meeting (IEDM), pp. 18–211824 (2021). https://doi.org/10.1109/IEDM19574.2021.9720616

Ko, K., Lee, J.K., Kang, M., Jeon, J., Shin, H.: Prediction of process variation effect for ultrascaled GAA vertical FET devices using a machine learning approach. IEEE Trans. Electron Devices 66(10), 4474–4477 (2019). https://doi.org/10.1109/TED.2019.2937786CrossRef

Ko, K., Lee, J.K., Shin, H.: Variability-aware machine learning strategy for 3-D NAND flash memories. IEEE Trans. Electron Devices 67(4), 1575–1580 (2020). https://doi.org/10.1109/TED.2020.2971784CrossRef

Hirtz, T., Huurman, S., Tian, H., Yang, Y., Ren, T.-L.: Framework for TCAD augmented machine learning on multi-I-V characteristics using convolutional neural network and multiprocessing. J. Semicond. 42(12), 124101 (2021). https://doi.org/10.1088/1674-4926/42/12/124101CrossRef

Mehta, K., Wong, H.Y.: Prediction of FinFET current–voltage and capacitance–voltage curves using machine learning with autoencoder. IEEE Electron Device Lett. 42(2), 136–139 (2021)CrossRef

Akbar, C., Li, Y., Sung, W.L.: Machine learning aided device simulation of work function fluctuation for multichannel gate-all-around silicon nanosheet MOSFETs. IEEE Trans. Electron Devices 68(11), 5490–5497 (2021). https://doi.org/10.1109/TED.2021.3084910CrossRef

Carrillo-Nuñez, H., Dimitrova, N., Asenov, A., Georgiev, V.: Machine learning approach for predicting the effect of statistical variability in Si junctionless nanowire transistors. IEEE Electron Device Lett. 40(9), 1366–1369 (2019). https://doi.org/10.1109/LED.2019.2931839CrossRef

Wang, J., Kim, Y.-H., Ryu, J., Jeong, C., Choi, W., Kim, D.: Artificial neural network-based compact modeling methodology for advanced transistors. IEEE Trans. Electron Devices 68(3), 1318–1325 (2021). https://doi.org/10.1109/TED.2020.3048918CrossRef

10.

Han, S.-C., Choi, J., Hong, S.-M.: Acceleration of semiconductor device simulation with approximate solutions predicted by trained neural networks. IEEE Trans. Electron Devices 68(11), 5483–5489 (2021). https://doi.org/10.1109/TED.2021.3075192CrossRef

11.

Yang, Q., Qi, G., Gan, W., Wu, Z., Yin, H., Chen, T., Hu, G., Wan, J., Yu, S., Lu, Y.: Transistor compact model based on multigradient neural network and its application in spice circuit simulations for gate-all-around Si cold source FETs. IEEE Trans. Electron Devices 68(9), 4181–4188 (2021). https://doi.org/10.1109/TED.2021.3093376CrossRef

12.

Li, M., İrsoy, O., Cardie, C., Xing, H.G.: Physics-inspired neural networks for efficient device compact modeling. IEEE J. Exploratory Solid-State Comput. Devices Circuits 2, 44–49 (2017). https://doi.org/10.1109/JXCDC.2016.2636161CrossRef

13.

Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S., Yang, L.: Physics-informed machine learning. Nat. Rev. Phys. 3(6), 422–440 (2021)CrossRef

14.

Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, New York (2009)

15.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning (ICML), vol. 37, pp. 448–456 (2015)

16.

Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)

17.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90

18.

Luo, Z., Shapira, S., Stiles, K.: Correlations of MOSFET transconductance: its physical origins and relevance to analog MOSFET modeling and operation. IEEE Trans. Electron Devices 50(3), 853–858 (2003). https://doi.org/10.1109/TED.2003.811391CrossRef

19.

Yuan, Z., Yu, Z.: Short-channel-effect modeling of DG-FETs using voltage-doping transformation featuring FD/PD modes. IEEE Electron Device Lett. 30(11), 1209–1211 (2009). https://doi.org/10.1109/LED.2009.2031503CrossRef

20.

Zhang, L., Chan, M.: Artificial neural network design for compact modeling of generic transistors. J. Comput. Electron. 16, 825–832 (2017)CrossRef

21.

Zhang, Z.: Improved adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–2 (2018). https://doi.org/10.1109/IWQoS.2018.8624183

Title: Characteristics prediction and optimization of InP HBT using machine learning
Authors: Xiao Jie
Jie Wang
Xinjian Ouyang
Yuan Zhuang
Zhilong Wang
Shuzhen You
Dawei Wang
Zhiping Yu
Publication date: 26-02-2024
Publisher: Springer US
Published in: Journal of Computational Electronics / Issue 2/2024
Print ISSN: 1569-8025
Electronic ISSN: 1572-8137
DOI: https://doi.org/10.1007/s10825-024-02139-8

Correction: Revealing structural, elastic, optoelectronic and thermoelectric properties of lead-free Ba2XTiO6 (X = Hf, Ce, Te) double perovskite for solar cells applications

Correction

Oscillations of transverse magnetoresistance in the conduction band of quantum wells at different temperatures and magnetic fields

Triple-metal gate work function engineering to improve the performance of junctionless cylindrical gate-all-around Si nanowire MOSFETs for the upcoming sub-3-nm technology node

Novel analytical STFT expressions for nonlinear power engineering problem solving

Numerical investigation of energy level strategy for TMO/Si tunneling heterojunction solar cells

Prefabrication design, theoretical framework and simulation demonstration of a meander-shaped MEMS piezoresistive pressure sensor implanted on silicon substrate circular diaphragm for enhancement of key performance parameters utilized for low-pressure applications

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Correction: Revealing structural, elastic, optoelectronic and thermoelectric properties of lead-free Ba2XTiO6 (X = Hf, Ce, Te) double perovskite for solar cells applications

Oscillations of transverse magnetoresistance in the conduction band of quantum wells at different temperatures and magnetic fields

Triple-metal gate work function engineering to improve the performance of junctionless cylindrical gate-all-around Si nanowire MOSFETs for the upcoming sub-3-nm technology node

Novel analytical STFT expressions for nonlinear power engineering problem solving

Numerical investigation of energy level strategy for TMO/Si tunneling heterojunction solar cells

Prefabrication design, theoretical framework and simulation demonstration of a meander-shaped MEMS piezoresistive pressure sensor implanted on silicon substrate circular diaphragm for enhancement of key performance parameters utilized for low-pressure applications