A dynamic sampling methodology for plasma etch processes using Gaussian process regression

Abstract

Plasma etch is a key process in modern semiconductor manufacturing facilities as it offers process simplification and yet greater dimensional tolerances compared to wet chemical etch technology. The main challenge of operating plasma etchers is to maintain a consistent etch rate spatially and temporally for a given wafer and for successive wafers processed in the same etch tool. Etch rate measurements require expensive metrology steps and therefore in general only limited sampling is performed. Furthermore, the results of measurements are not accessible in real-time, limiting the options for run-to-run control. This paper investigates a Virtual Metrology (VM) enabled Dynamic Sampling (DS) methodology as an alternative paradigm for balancing the need to reduce costly metrology with the need to measure more frequently and in a timely fashion to enable waferto-wafer control. Using a Gaussian Process Regression (GPR) VM model for etch rate estimation of a plasma etch process, the proposed dynamic sampling methodology is demonstrated and evaluated for a number of different predictive dynamic sampling rules.

Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework

“…The main event-based SDS strategy is called uncertainty sampling (Lewis and Gale 1994). It consists of triggering a measurement when the estimation is no longer trusted (Kurz, De Luca, and Pilz 2015;Susto 2017), out of tolerance (Wan, Honari, and McLoone 2013), or impossible, as with just-in-time (JIT) learning extrapolation problems (Jebri et al 2017a). The first approach, namely uncertainty sampling, has been integrated into all SDSs across the SOTA papers on VM.…”

Virtual metrology modeling of time-dependent spectroscopic signals by a fused lasso algorithm

“…Generally, the first few features extracted by PCR or PLS are sufficient to satisfy the objective of an analysis; hence, these methods have been widely applied in the VM modeling of high-dimensional data such as spectroscopic signals. Wan et al investigated a Gaussian process regression-based VM model for predicting etch rate using spectroscopic signals [9]. Prior to training the VM model, they mapped the large input space to a reduced latent feature space using PLS.…”

“…The spectroscopic signals consist of two factors-wavelength and time. However, previous studies have focused on reduced datasets summarized by their statistical measures such as mean, variance, maximum, and minimum [9,11,21]. Because plasma processing is a dynamic process, preserving time information is crucial [22].…”

Meta-Learned and TCAD-Assisted Sampling in Semiconductor Laser Annealing

“…In the case of optimization, the experiment can be conducted using a selected optimization algorithm, which has been a mature field. On the other hand, in the case of constructing an accurate model for advanced process development or predictive maintenance, data-efficient sampling in semiconductor manufacturing has been less studied compared to optimization, while sampling has been an intensively studied subject in other fields. − Prior works in data-efficient sampling in the semiconductor process are mainly in yield improvement, quality control, and predictive maintenance in a production-line setting. − On the other hand, the sampling strategies in developing key process steps in advanced technology nodes have not been studied much, and we only found limited literature. , …”

“…7−14 On the other hand, the sampling strategies in developing key process steps in advanced technology nodes have not been studied much, and we only found limited literature. 14,15 Laser annealing is promising in advanced semiconductor technology nodes due to its low thermal budget and capability of localized annealing. Traditional furnace annealing cannot fully eliminate defects and can result in the degradation of material properties, the precipitation of dopant substances, and the lateral dopant diffusion in wafers owing to high temperature and anneal time.…”