Neural Network Model of Plasma Charging Damage on MOSFET Device

Abstract

Plasma-induced charging damage can degrade device reliability considerably. Constructing charging damage model is important since it can be used for qualitative parameter effect analysis as well as for process optimization. In this study, a backpropagation neural network was used to capture causal relationships between process parameters and plasma charging-damaged metal-oxide semiconductor (MOS) device. For a systematic modeling, plasma charging damage process was characterized by means of a face-centered Box Wilson experiment. As a charge collector, aluminum antennae were connected to MOS device. The prediction performance of BPNN was optimized as a function of training factors. Electrical device characteristics modeled include threshold voltage (V), subthreshold swing (S), and transconductance (G). The normalized prediction errors are 1.84, 2.99, and 1.87{%} for V, S, and G, respectively. The optimized models were compared to statistical regression and fuzzy logic models. The comparison revealed that neural network models yielded more improved predictions over either of them. The improvement was even more than 40{%} for V and S.

Keywords:

• Fuzzy logic model
• Model
• Neural network
• Plasma charging damage
• Plasma etching
• Reliability
• Semiconductor process
• Statistical regression model
• Threshold voltage
• Transconductance
• Subthreshold swing
• Yield

ACKNOWLEDGMENTS

This work was supported by the Seoul R&BD Program (Grant No.10583). The authors are thankful to ETRI technical staffs for the experimental plasma charging data.