TCAD-Enabled Machine Learning—An Efficient Framework to Build Highly Accurate and Reliable Models for Semiconductor Technology Development and Fabrication

Jungmann, Paul; Johnson, J. B.; Cruz‐Silva, Eduardo; Taylor, William J.; Khan, Abdul Hanan; Kumar, Akash

doi:10.1109/tsm.2023.3240033

Cited by 3 publications

(1 citation statement)

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“…Even with the limited datasets available to date, utility in studying wide bandgap semiconducting datasets with ML has been reported. Success has been demonstrated using computationally generated datasets from TCAD simulations 2,3 , which can predict CV and IV behavior of Ga 2 O 3 Schottky Barrier Diodes [4][5][6] . Some experimental ML models have been used to predict transistor current and switching voltage from gate currents and input voltages 7 .…”

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confidence: 99%

Detecting defects that reduce breakdown voltage using machine learning and optical profilometry

Gallagher,

Mastro,

Jacobs

et al. 2024

Sci Rep

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Semiconductor wafer manufacturing relies on the precise control of various performance metrics to ensure the quality and reliability of integrated circuits. In particular, GaN has properties that are advantageous for high voltage and high frequency power devices; however, defects in the substrate growth and manufacturing are preventing vertical devices from performing optimally. This paper explores the application of machine learning techniques utilizing data obtained from optical profilometry as input variables to predict the probability of a wafer meeting performance metrics, specifically the breakdown voltage (Vbk). By incorporating machine learning techniques, it is possible to reliably predict performance metrics that cause devices to fail at low voltage. For diodes that fail at a higher (but still below theoretical) breakdown voltage, alternative inspection methods or a combination of several experimental techniques may be necessary.

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confidence: 99%