Machine Learning on Multiplexed Optical Metrology Pattern Shift Response Targets to Predict Electrical Properties

Abstract

Doing high throughput high accuracy metrology in small geometries is challenging. One approach is to build easily measurable proxy targets onto dies and make a predictive model based on those signals. We use optical Pattern Shift Response (PSR) proxy targets to build predictive models of the electrical characteristics of devices in the Back End Of Line (BEOL). Given the wide choice of PSR targets, we explore how to select combinations of them to maximise the utility of the features for building an accurate Machine Learning (ML) model; we call this approach Multiplexed Optical Metrology. We also explore the trade-off between chip area dedicated to targets and achievable accuracy. We run ML experiments using different selections of targets measured at different stages of BEOL processing: postlithography and post-Chemical-Mechanical-Planarisation (CMP). Our results show that a) reasonable predictive performance can be achieved for a reasonable area budget; b) ML model performance across target families varies significantly, thus justifying the need for careful selection of targets; c) longitudinal measurements of targets increases accuracy for no extra area penalty; d) increasing the the number of targets gives some improvement in accuracy for a dataset of this size, but relatively small compared to the increase in area budget needed.Ultimately we aim to do die-level yield prediction using these techniques. We discuss how collecting a larger dataset with appropriate yield information is the logical next step to achieving this.

Advancing High-Volume GaN Manufacturing: Precision Simulation of Electrical and Geometrical Deviations

“…Kim et al harnessed deep learning techniques, including Focal Loss, PixelGAN, and multi-scale level features, to identify defects in GaN wafers [22]. Ashby et al developed the Multiplexed Optical Metrology model, which accurately predicts the electrical characteristics of back-end manufactured dies [23]. These efforts collectively address the critical need for quality assurance in GaN-based manufacturing.…”

Advancing High-Volume GaN Manufacturing: Precision Simulation of Electrical and Geometrical Deviations

“…Kim et al harnessed deep learning techniques, including Focal Loss, PixelGAN, and multi-scale level features, to identify defects in GaN wafers [22]. Ashby et al developed the Multiplexed Optical Metrology model, which accurately predicts the electrical characteristics of back-end manufactured dies [23]. These efforts collectively address the critical need for quality assurance in GaN-based manufacturing.…”