Automatic Prediction of Metal–Oxide–Semiconductor Field‐Effect Transistor Threshold Voltage Using Machine Learning Algorithm

Choi, Seoyeon; Park, Dong Geun; Kim, Min Jung; Bang, Seain; Kim, Jungchun; Jin, Seunghee; Huh, Ki Seok; Kim, Dong-Hyun; Mitard, J.; Han, Cheol E.; Lee, Jae Woo

doi:10.1002/aisy.202200302

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…The performance of all electronic devices, including diodes [35,36], bipolar junction transistors [37,38], field effect transistors (FETs) [39][40][41], metal oxide semiconductor field effect transistors (MOSFETs) [42], even optoelectronic devices such as light emitting diodes (LEDs) [43][44][45], lasers [46][47][48], solar cells [49][50][51] and photodetectors [52][53][54] depend upon the charge transport within the device material. The charge carriers in semiconductors are electrons and holes, which are available in the conduction band and valence band respectively.…”

Section: Introductionmentioning

confidence: 99%

Review on Charge Carrier Transport in Inorganic and Organic Semiconductors

Morab,

Sundaram,

Pivrikas

2023

Coatings

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Inorganic semiconductors like silicon and germanium are the foundation of modern electronic devices. However, they have certain limitations, such as high production costs, limited flexibility, and heavy weight. Additionally, the depletion of natural resources required for inorganic semiconductor production raises concerns about sustainability. Therefore, the exploration and development of organic semiconductors offer a promising solution to overcome these challenges and pave the way for a new era of electronics. New applications for electronic and optoelectronic devices have been made possible by the recent emergence of organic semiconductors. Numerous innovative results on the performance of charge transport have been discovered with the growth of organic electronics. These discoveries have opened up new possibilities for the development of organic electronic devices, such as organic solar cells, organic light-emitting diodes, and organic field-effect transistors. The use of organic materials in these devices has the potential to revolutionise the electronics industry by providing low-cost, flexible, and lightweight alternatives to traditional inorganic materials. The understanding of charge carrier transport in organic semiconductors is crucial for the development of efficient organic electronic devices. This review offers a thorough overview of the charge carrier transport phenomenon in semiconductors with a focus on the underlying physical mechanisms and how it affects device performance. Additionally, the processes of carrier generation and recombination are given special attention. Furthermore, this review provides valuable insights into the fundamental principles that govern the behaviour of charge carriers in these materials, which can inform the design and optimisation of future devices.

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Section: Introductionmentioning

confidence: 99%

Review on Charge Carrier Transport in Inorganic and Organic Semiconductors

Morab,

Sundaram,

Pivrikas

2023

Coatings

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A Random Forest Model for Predicting and Analyzing the Performance of CNT TFET with Highly Doped Pockets

Salah,

Yevick

2024

Advcd Theory and Sims

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This paper presents a Random Forest (RF) machine learning model that relates the DC characteristics and high‐frequency response of a carbon nanotube (CNT) tunnel field‐effect transistor (TFET) with highly doped pockets to the transistor parameters. The analysis of multiple factors for a complex structure as the one studied here becomes expensive with the ordinary simulation techniques and hence machine learning (ML) offers a proficient method to model and enhance the understanding of the key factors that influence the CNT TFET with pockets in considerably reduced time. Numerical simulations are used to generate the data on which the model is trained. This dataset comprises ten input features and four output attributes. The tuned model is capable of predicting the output characteristics of the device with minimal mean squared error (MSE). The RF model is also compared to other ML algorithms to demonstrate its advantage.

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Machining quality prediction of multi-feature parts using integrated multi-source domain dynamic adaptive transfer learning

Wang,

Qi,

et al. 2024

Robotics and Computer-Integrated Manufacturing

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Automatic Prediction of Metal–Oxide–Semiconductor Field‐Effect Transistor Threshold Voltage Using Machine Learning Algorithm

Abstract: The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aisy.202200302.

Cited by 4 publications

References 38 publications

Review on Charge Carrier Transport in Inorganic and Organic Semiconductors

Review on Charge Carrier Transport in Inorganic and Organic Semiconductors

A Random Forest Model for Predicting and Analyzing the Performance of CNT TFET with Highly Doped Pockets

Machining quality prediction of multi-feature parts using integrated multi-source domain dynamic adaptive transfer learning

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