Search query: gcib etch semiconductor

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abstract: Shrinking process windows for advanced processing of complex devices, sub 14nm, require advanced topography control. Within wafer topography variations impact the uniformity of subsequent layers and can affect yield. Process tools can generally control global uniformity across wafer, but are not well equipped to fine tune the local (reticle to reticle) topography. Advanced new process tools such as the Gas Cluster Ion Beam (GCIB) offer a promising path for improving topography within wafer and from wafer to wafer [1, 2] GCIB uses a highly localized focus beam controlled by location specific processing (LSP) algorithms to achieve the needed planarization corrections. In order to be effective, the LSP algorithms require wafer specific knowledge on the incoming topography distribution. The beam dwells in different locations on the wafer for different amounts of time in order to vary the amount of material removed. Therefore, the performance of local planarization tools depends on the availability and quality of the metrology data. Integrated scatterometry based metrology (IM) is the workhorse metrology enabler for inline APC (Advanced Process Control) and monitoring solutions for the Chemical Mechanical Planarization (CMP) and Reactive Ion Etching (RIE) processes. While maintaining equivalent performance to their standalone (SA) scatterometry siblings, IMs are typically mounted on the GCIB tool and dedicated to provide pre and post process measurements for wafers processed on the tool. They enable real time per wafer adjustments to within lot process knobs due to their proximity to the process.
author_names: Taher Kagalwala

Ravi K Dasaka

Michael V Aquilino

Laertis Economikos

Aron J Cepler

Charles Kang

Naren Yellai
title: Integrated metrology's role in Gas Cluster Ion Beam etch
venue: 2015 26th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)
year: 2015

abstract: Argon Gas Cluster Ion Beam (GCIB) sources have become very popular in XPS and SIMS in recent years, due to the minimal chemical damage they introduce in the depth profiling of polymer and other organic materials. These GCIB sources are therefore particularly useful for depth profiling polymer and organic materials, but also (though more slowly) the surfaces of inorganic materials such as semiconductors, due to the lower roughness expected in cluster ion sputtering compared to that introduced by monatomic ions. We have examined experimentally a set of five compound semiconductors, cadmium telluride (CdTe) gallium arsenide (GaAs) gallium phosphide (GaP) indium arsenide (InAs) and zinc selenide (ZnSe) and a high k dielectric material, hafnium oxide (HfO) in their response to argon cluster profiling. An experimentally determined HfO etch rate of 0.025 nm/min (3.95 x 10 2 amu/atom in ion) for 6 keV Ar gas clusters is used in the depth scale conversion for the profiles of the semiconductor materials. The a.
author_names: Anders J Barlow

Jose F Portoles

Peter J Cumpson
title: Observed damage during Argon gas cluster depth profiles of compound semiconductors
year: 2014

abstract: We use optical, thermal, and electrical simulation to evaluate the effects of using varying amounts of undercut etch on wavelength scale and subwavelength metal clad semiconductor nanolasers (MCSELs) We find that as MCSEL diameter decreases, the optical performance becomes more sensitive to slight amounts of sidewall tilt. A modest amount of undercut (25% dramatically improves the optical performance, reducing modal threshold gain to 100 cm 1 or less for lasers with core radius of 225, 550, or 775 nm, even in the presence of significant sidewall tilt (20deg gain sidewall or 8deg pedestal sidewall tilt) Finally, we examine the effects of the increased undercut on nanolaser thermal performance and find that the increased resistive heating is insignificant near threshold, even for subwavelength nanolasers.
author_names: Janelle C Shane

Qing Gu

Alan Potterton

Yeshaiahu Fainman
title: Effect of Undercut Etch on Performance and Fabrication Robustness of Metal Clad Semiconductor Nanolasers
venue: IEEE Journal of Quantum Electronics
year: 2015

abstract: We show that Au nanoparticles spontaneously move across the (001) surface of InP, InAs, and GaP when heated in the presence of water vapor. As they move, the particles etch crystallographically aligned grooves into the surface. We show that this process is a negative analogue of the vapor liquid solid (VLS) growth of semiconductor nanowires: the semiconductor dissolves into the catalyst and reacts with water vapor at the catalyst surface to create volatile oxides, depleting the dissolved cations and anions and thus sustaining the dissolution process. This VLS etching process provides a new tool for directed assembly of structures with sublithographic dimensions, as small as a few nanometers in diameter. Au particles above 100 nm in size do not exhibit this process but remain stationary, with oxide accumulating around the particles.
author_names: Babak Nikoobakht

Andrew A Herzing

Shin Muramoto

Jerry Tersoff
title: Vapor Liquid Solid Etch of Semiconductor Surface Channels by Running Gold Nanodroplets.
venue: Nano letters
year: 2015

abstract: Abstract Reflectance anisotropy spectroscopy (RAS) can be used to monitor (reactive) ion etching (RIE) of semiconductor samples. We present results on the influence of the Cl2 content of the plasma gas on the RAS spectra during reactive ion etching. In a first step GaAs samples have been used and the RAS spectra are compared to results of secondary ion mass spectrometry (SIMS) on sample surfaces and depth profiles. In a second step a III V semiconductor multilayer system has been investigated using the time evolution of the average reflected intensity as an indication for the etch rate. In both cases usually even a high amount of Cl2 does not disturb the surface sensitivity of the RAS signal.
author_names: Lars Barzen

Annika Kleinschmidt

Johannes Strassner

Christoph Doering

Henning Fouckhardt

W Bock

Michael Wahl

Michael Kopnarski
title: Influence of plasma composition on reflectance anisotropy spectra for in situ III V semiconductor dry etch monitoring
year: 2015

abstract: A Bayesian network (BN) based fault diagnosis framework for semiconductor etching equipment is presented. Suggested framework contains data preprocessing, data synchronization, time series modeling, and BN inference, and the established BNs show the cause and effect relationship in the equipment module level. Statistically significant state variable identification (SVID) data of etch equipment are preselected using principal component analysis (PCA) and derivative dynamic time warping (DDTW) is employed for data synchronization. Elman's recurrent neural networks (ERNNs) for individual SVID parameters are constructed, and the predicted errors of ERNNs are then used for assigning prior conditional probability in BN inference of the fault diagnosis. For the demonstration of the proposed methodology, 300 mm etch equipment model is reconstructed in subsystem levels, and several fault diagnosis scenarios are considered. BNs for the equipment fault diagnosis consists of three layers of nodes, such as root cause (RC) module (M) and data parameter (DP) and the constructed BN illustrates how the observed fault is related with possible root causes. Four out of five different types of fault scenarios are successfully diagnosed with the proposed inference methodology.
author_names: Javeria Muhammad Nawaz

Muhammad Zeeshan Arshad

Sang Jeen Hong
title: Fault Diagnosis in Semiconductor Etch Equipment Using Bayesian Networks
year: 2014

abstract: A method of fault detection and classification (FDC) for semiconductor manufacturing equipment e diagnostics using equipment data is presented. Detecting faulty processes, identifying any anomaly at their onsets, and rapidly classifying the root cause of the fault are crucial for maximizing equipment utilization in current semiconductor manufacturing; however, tool data acquired from production equipment contains much information that is often challenging to analyze due to its sheer volume and complexity. In this paper, modular neural network (MNN) modeling is presented as a method for fault detection modeling in plasma etching. Based on the result from the MNN modeling, a tool data set is grouped according to its related subsystems, and FDC is performed using Dempster Shafer (D S) theory to address the uncertainty associated with fault diagnosis. Subsystem level fault detections, such as radio frequency (RF) power source module, RF power bias module, gas delivery module, and process chamber module, are presented by combining related parameters, and successful fault detection is achieved. The evidential reasoning of RF probe is also beneficial for the detection of chamber leak simulation, and the classification of fault is made by further investigating voltage signal of RF probe. Successful fault detection in subsystem level with zero missed alarms was demonstrated using D S theory of evidential reasoning, and the classification for finding root cause of the fault is presented in the chamber leak fault simulation. We realized that successful FDC can be accomplished by combining various related information and by incorporating engineering expert knowledge.
author_names: Sang Jeen Hong

Woo Yup Lim

Taesu Cheong

Gary S May
title: Fault Detection and Classification in Plasma Etch Equipment for Semiconductor Manufacturing $e$ Diagnostics
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2012

abstract: This article addresses a challenging resource constrained flow shop scheduling problem from automated wet etch stations (AWSs) in wafer fabrication of semiconductor manufacturing facilities by means of a constraint programming (CP) methodology. Wet etching in wafer fabrication is a difficult process considering the material handling limitations and mixed intermediate policies. The proposed integrated approach consists of both a CP model and an efficient search strategy in order to handle the different features of the process. The domain specific search strategy significantly improves the computational performance, a key aspect given the high combinatorial complexity of the problem. The search strategy objective is to avoid losing time due to early bad choices in the exploration and to produce good quality solutions with low computational effort. The applicability of the proposed integrated CP methodology is successfully tested with several examples taken from the literature, featuring a different number o.
author_names: Luis Zeballos

Pedro M Castro

Carlos A Mendez
title: Integrated Constraint Programming Scheduling Approach for Automated Wet Etch Stations in Semiconductor Manufacturing
year: 2011

abstract: Abstract This work addresses the short term scheduling of one of the most critical stages in the semiconductor industry, the automated wet etch station (AWS) An efficient MILP based computer aided tool is developed in order to achieve a proper synchronization between the activities of sequential chemical and water baths and limited automated wafer's lot transfer devices. The major goal is to find the optimal integrated schedule that maximizes the whole process productivity without generating wafer contamination. Several examples are successfully solved to illustrate the capabilities of the proposed method.
author_names: Adrian M Aguirre

Carlos A Mendez

Pedro M Castro
title: A novel optimization method to automated wet etch station scheduling in semiconductor manufacturing systems
venue: Comput. Chem. Eng.
year: 2011

abstract: The objective of this paper is to develop a wafer by wafer fault detection model for a semiconductor etch tool operating in a worksite situation in which the tool parameter traces are correlated and drift slowly from an initial recipe setting. Process drift is a common occurrence in many processes because of the aging of tool components. The proposed fault detection model compares the entire trace structures of the tool parameters with reference templates by using an improved DTW (dynamic time warping) algorithm, and it performs a T 2 based multivariate analysis with the structure similarity scores created by the improved DTW. In addition, to adapt to the process drift, a recursive T 2 update procedure with an optimal correction factor is incorporated in the model. The optimal correction factor is derived using the Kalman filtering technique. Experiments using the data collected from a worksite reactive ion etching process demonstrate that the performance of the proposed fault detection model is very encouraging.
author_names: Jong Myoung Ko

Chang Ouk Kim
title: A multivariate parameter trace analysis for online fault detection in a semiconductor etch tool
year: 2012