Prediction of electrical properties of FDSOI devices based on deep learning

Rong Zhao; Shulong Wang; Xiaoling Duan; Chenyu Liu; Lan Ma; Shupeng Chen; Hongxia Liu

doi:10.1088/1361-6528/ac6c95

Prediction of electrical properties of FDSOI devices based on deep learning

Nanotechnology. 2022 May 27;33(33). doi: 10.1088/1361-6528/ac6c95.

Authors

Rong Zhao¹, Shulong Wang¹, Xiaoling Duan¹, Chenyu Liu¹, Lan Ma¹, Shupeng Chen¹, Hongxia Liu¹

Affiliation

¹ School of Microelectronics, Xidian University, Xi 'an 710071, People's Republic of China.

PMID: 35508081
DOI: 10.1088/1361-6528/ac6c95

Abstract

Fully depleted Silicon on insulator technology (FDSOI) is proposed to solve the various non-ideal effects when the process size of integrated circuits is reduced to 45 nm. The research of traditional FDSOI devices is mostly based on simulation software, which requires a lot of calculation and takes a long time. In this paper, a deep learning (DL) based electrical characteristic prediction method for FDSOI devices is proposed. DL algorithm is used to train the simulation data and establish the relationship between the physical parameters and electrical characteristics of the device. The network structure used in the experiment has high prediction accuracy. The mean square error of electrical parameters and transfer characteristic curve is only 4.34 × 10^-4and 2.44 × 10^-3respectively. This method can quickly and accurately predict the electrical characteristics of FDSOI devices without microelectronic expertise. In addition, this method can be extended to study the effects of various physical variables on device performance, which provides a new research method for the field of microelectronics.

Keywords: FDSOI devices; deep learning; electrical properties; neural networks; transfer characteristic.