NDLI: In-situ prediction of RIE time to completion using neural networks

Content Provider	IEEE Xplore Digital Library
Author	Baker, D. Himmel, C.D. May, G.S.
Copyright Year	1994
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA (Baker, D.; Himmel, C.D.; May, G.S.)
Abstract	Reactive ion etching (RIE) in radio frequency glow discharges is perhaps the most popular means of achieving the level of detail necessary to pattern small geometry features in electronics manufacturing. However, the complexity of the RIE process has prompted the use of empirical models utilizing neural networks, which offer advantages in both accuracy and robustness over statistical methods. In this paper, a neural network trained to model the correlation between DC bias and etch rate was used to predict the time required to remove a specified thickness of silicon dioxide (SiO/sub 2/) in a CHF/sub 3//O/sub 2/ plasma. A real-time data acquisition system that transmits process conditions from a Plasma Therm 700 series RIE system was used to monitor DC bias during etching. A back-propagation neural network was trained to predict the amount of time required to etch the remaining amount of film while in the midst of etching. Inputs to the network included elapsed time during the etch run, the desired etch depth, gas flow rates, chamber pressure, and RF power. This network exhibited a 26-second RMS error on training data, and predicted the process endpoint on a set of test etch recipes with an average error of less than two minutes for a process time of about 25 minutes.<>
Starting Page	319
Ending Page	324
File Size	562965
Page Count	6
File Format	PDF
ISBN	0780320379
DOI	10.1109/IEMT.1994.404725
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-09-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Etching Radio frequency Plasma applications Glow discharges Geometry Manufacturing Robustness Statistical analysis Predictive models
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

In-situ prediction of reactive ion etch endpoint using neural networks

Modeling reactive ion etching of silicon dioxide films using neural networks

Real-time plasma etch control using in-situ sensors and neural networks

Dynamic behaviors and stability of atmospheric glow plasmas at 1 - 10 MHz

Measurements and models of ion energy distributions in high-density, radio-frequency-biased CF/sub 4/ discharges

Plasma Etched Quartz SAW Resonators

Characterization of inductively coupled plasma using neural networks

Dynamic neural control for a plasma etch process

The modelling of plasma etching processes using neural network and statistical techniques

In-situ prediction of RIE time to completion using neural networks

Similar Documents

In-situ prediction of reactive ion etch endpoint using neural networks

Modeling reactive ion etching of silicon dioxide films using neural networks

Real-time plasma etch control using in-situ sensors and neural networks

Dynamic behaviors and stability of atmospheric glow plasmas at 1 - 10 MHz

Measurements and models of ion energy distributions in high-density, radio-frequency-biased CF/sub 4/ discharges

Plasma Etched Quartz SAW Resonators

Characterization of inductively coupled plasma using neural networks

Dynamic neural control for a plasma etch process

The modelling of plasma etching processes using neural network and statistical techniques

In-situ prediction of RIE time to completion using neural networks