Deep Learning Approach to Inverse Grain Pattern of Nanosized Metal Gate for Multichannel Gate-All-Around Silicon Nanosheet MOSFETs

• Published in: IEEE transactions on semiconductor manufacturing Vol. 34; no. 4; pp. 513 - 520
• Main Authors: Akbar, Chandni, Li, Yiming, Sung, Wen-Li
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural network; Artificial neural networks; Circuit design; CMOS; Computer simulation; Deep learning; Fluctuations; gate-all-around; Learning theory; Machine learning; MOSFET; MOSFETs; nanosheet; Nanosheets; Nanotechnology; Nanotechnology devices; Optimization; Semiconductor device modeling; Silicon; Variation; work function fluctuation; Work functions
• ISSN: 0894-6507; 1558-2345
• DOI: 10.1109/TSM.2021.3116250

For the first time, a deep learning (DL) algorithm is presented to study the effect of the source of variability on the performance of semiconductor nanodevice. This paper reports the possibility of an alternative solution of device simulation in order to optimize the source variation. It is based on the statistical distribution of work function fluctuation (WKF) on the metal gate depending on the orientation and location of metal grains. It has been revealed that the WKF of a metal gate can lead to different fluctuations in electrical characteristics. Therefore, an emerging DL algorithm, artificial neural network (ANN) is utilized to identify the appropriate WKF patterns on the metal gate that can reduce the impact of characteristic fluctuation, i.e., <inline-formula> <tex-math notation="LaTeX">\sigma V_{TH}, \sigma I_{ON} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\sigma I_{OFF} </tex-math></inline-formula>, simultaneously. The application of the DL-ANN algorithm to multichannel gate-all-around silicon nanosheet MOSFETs is explored to suppress the effect of WKF on the characteristic fluctuation. Consequently, it can be further utilized to investigate the implication of WKF for the process variation, modeling the nanodevices and analysis of circuit design. Notably, this technique can be extended to study the diverse random sources and process variation effects for emerging nano-CMOS devices and can effectively accelerate the device simulation and optimization.