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Artificial Neural Network (ANN)-Based Model To Evaluate The Characteristics of A Nanosheet FET (NSFET)

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This new technical paper titled “Machine-Learning-Based Compact Modeling for Sub-3-nm-Node Emerging Transistors” was published by researchers at SungKyunKwan University, Korea.

Abstract:
“In this paper, we present an artificial neural network (ANN)-based compact model to evaluate the characteristics of a nanosheet field-effect transistor (NSFET), which has been highlighted as a next-generation nano-device. To extract data reflecting the accurate physical characteristics of NSFETs, the Sentaurus TCAD (technology computer-aided design) simulator was used. The proposed ANN model accurately and efficiently predicts currents and capacitances of devices using the five proposed key geometric parameters and two voltage biases. A variety of experiments were carried out in order to create a powerful ANN-based compact model using a large amount of data up to the sub-3-nm node. In addition, the activation function, physics-augmented loss function, ANN structure, and preprocessing methods were used for effective and efficient ANN learning. The proposed model was implemented in Verilog-A. Both a global device model and a single-device model were developed, and their accuracy and speed were compared to those of the existing compact model. The proposed ANN-based compact model simulates device characteristics and circuit performances with high accuracy and speed. This is the first time that a machine learning (ML)-based compact model has been demonstrated to be several times faster than the existing compact model.”

Find the technical paper here. Published Sept 2022.

Woo, S.; Jeong, H.; Choi, J.; Cho, H.; Kong, J.-T.; Kim, S. Machine-Learning-Based Compact Modeling for Sub-3-nm-Node Emerging Transistors. Electronics 2022, 11, 2761. https://doi.org/10.3390/electronics11172761.

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