Machine learning aided process control: critical dimension uniformity control of etching process in 1z nm DRAM

Taeyong Jo; Insoo Choi; Doocheol Choi; Yoonsung Bae; Seunggun Byoun; Inho Kim; Sukwon Lee; Changhoon Choi; Euiseok Kum; Youngil Kang; Taejoong Kim; Youngjoo Lee

doi:10.1117/12.2583473

17 March 2021 Machine learning aided process control: critical dimension uniformity control of etching process in 1z nm DRAM

Taeyong Jo, Insoo Choi, Doocheol Choi, Yoonsung Bae, Seunggun Byoun, Inho Kim, Sukwon Lee, Changhoon Choi, Euiseok Kum, Youngil Kang, Taejoong Kim, Youngjoo Lee

Author Affiliations +

Proceedings Volume 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV; 116111L (2021) https://doi.org/10.1117/12.2583473
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

Conventional semiconductor etching process control has been performed by separated steps: process, metrology, and feedback control. Uniformity of structures such as Critical Dimension (CD) is an important factor in determining completeness of etching process. To achieve better uniformity, several feedback control has been performed. However, it is difficult to give feedback to the process after metrology due to the lack of process knowledge. In this study, we propose a machine learning technique that can create process control commands from the measured structure using a miniaturized Integrated Metrology (IM) of Spectroscopic Ellipsometery (SE) form. And it is possible to learn the physical analysis through machine learning without introducing a physical analysis method. The proposed analysis consists of two machine learning part: the first neural network for CD metrology, and second network for command generation. The first neural network takes a spectrum sampled at 2048 wavelengths obtained from IM as an input, and outputs CDs of structures. Finally, the second artificial neural network takes a changes of temperatures in a wafer and outputs the control commands of powers. As a result, we have improved the CD range of poly mask in a wafer from 1.69 nm to 1.36 nm.

Conference Presentation

Citation Download Citation

Taeyong Jo, Insoo Choi, Doocheol Choi, Yoonsung Bae, Seunggun Byoun, Inho Kim, Sukwon Lee, Changhoon Choi, Euiseok Kum, Youngil Kang, Taejoong Kim, and Youngjoo Lee "Machine learning aided process control: critical dimension uniformity control of etching process in 1z nm DRAM", Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 116111L (17 March 2021); https://doi.org/10.1117/12.2583473

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available