Example of IEEE Transactions on Semiconductor Manufacturing format
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Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format
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Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format Example of IEEE Transactions on Semiconductor Manufacturing format
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open access Open Access

IEEE Transactions on Semiconductor Manufacturing — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Industrial and Manufacturing Engineering #83 of 336 up up by 7 ranks
Electrical and Electronic Engineering #203 of 693 up up by 61 ranks
Electronic, Optical and Magnetic Materials #75 of 246 up up by 29 ranks
Condensed Matter Physics #127 of 411 up up by 67 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 293 Published Papers | 1277 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 30/06/2020
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Related Journals

open access Open Access

IEEE

Quality:  
High
CiteRatio: 7.2
SJR: 1.023
SNIP: 1.249
open access Open Access

Elsevier

Quality:  
High
CiteRatio: 6.5
SJR: 0.888
SNIP: 0.744
open access Open Access
recommended Recommended

Wiley

Quality:  
High
CiteRatio: 16.6
SJR: 2.286
SNIP: 2.274

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.977

73% from 2018

Impact factor for IEEE Transactions on Semiconductor Manufacturing from 2016 - 2019
Year Value
2019 1.977
2018 1.14
2017 1.336
2016 1.117
graph view Graph view
table view Table view

4.4

52% from 2019

CiteRatio for IEEE Transactions on Semiconductor Manufacturing from 2016 - 2020
Year Value
2020 4.4
2019 2.9
2018 2.6
2017 2.4
2016 2.7
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 73% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 52% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.732

11% from 2019

SJR for IEEE Transactions on Semiconductor Manufacturing from 2016 - 2020
Year Value
2020 0.732
2019 0.659
2018 0.44
2017 0.359
2016 0.429
graph view Graph view
table view Table view

1.305

2% from 2019

SNIP for IEEE Transactions on Semiconductor Manufacturing from 2016 - 2020
Year Value
2020 1.305
2019 1.275
2018 0.77
2017 0.874
2016 1.004
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 11% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

IEEE Transactions on Semiconductor Manufacturing

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IEEE

IEEE Transactions on Semiconductor Manufacturing

IEEE Transactions on Semiconductor Manufacturing addresses innovations of interest to the integrated circuit manufacturing researcher and professional. Includes advanced process control, equipment modeling and control, yield analysis and optimization, defect control, and manuf...... Read More

Engineering

i
Last updated on
29 Jun 2020
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ISSN
0894-6507
i
Impact Factor
High - 1.38
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
IEEEtran
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Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

Journal Article DOI: 10.1109/66.4384
Scheduling semiconductor wafer fabrication
Lawrence M. Wein1

Abstract:

The impact that scheduling can have on the performance of semi-conductor wafer fabrication facilities is assessed. The performance measure considered is the mean throughput time (sometimes called cycle time, turnaround time or manufacturing interval) for a lot of wafers. A variety of input control and sequencing rules are eva... The impact that scheduling can have on the performance of semi-conductor wafer fabrication facilities is assessed. The performance measure considered is the mean throughput time (sometimes called cycle time, turnaround time or manufacturing interval) for a lot of wafers. A variety of input control and sequencing rules are evaluated using a simulation model of a representative, but fictitious, semiconductor wafer fabrication. Certain of these scheduling rules are derived by restricting attention to the sub-set of stations that are heavily utilized, and by using a Brownian network model, which approximates a multi-class queuing network model with dynamic control capability. Three versions of the wafer fabrication model, which differ only by the number of servers present at particular stations, are studied. The three versions have one, two, and four stations, respectively, that are heavily utilized (near 90% utilization). The simulation results indicate that scheduling has a significant impact on average throughput time, with larger improvements coming from discretionary imput control than from lot sequencing. The effects that specific sequencing rules have are highly dependent on both the type of input control used and the number of bottleneck stations in the fabrication. > read more read less

Topics:

Wafer fabrication (60%)60% related to the paper, Round-robin scheduling (58%)58% related to the paper, Dynamic priority scheduling (57%)57% related to the paper, Fair-share scheduling (57%)57% related to the paper, Two-level scheduling (57%)57% related to the paper
643 Citations
Journal Article DOI: 10.1109/66.920723
Material removal mechanism in chemical mechanical polishing: theory and modeling
Jianfeng Luo1, David Dornfeld1

Abstract:

The abrasion mechanism in solid-solid contact mode of the chemical mechanical polishing (CMP) process is investigated in detail. Based on assumptions of plastic contact over wafer-abrasive and pad-abrasive interfaces, the normal distribution of abrasive size and an assumed periodic roughness of pad surface, a novel model is d... The abrasion mechanism in solid-solid contact mode of the chemical mechanical polishing (CMP) process is investigated in detail. Based on assumptions of plastic contact over wafer-abrasive and pad-abrasive interfaces, the normal distribution of abrasive size and an assumed periodic roughness of pad surface, a novel model is developed for material removal in CMP. The basic model is MRR=/spl rho//sub w/NVol/sub removed/, where /spl rho//sub w/ is the density of wafer N the number of active abrasives, and Vol/sub removed/ the volume of material removed by a single abrasive. The model proposed integrates process parameters including pressure and velocity and other important input parameters including the wafer hardness, pad hardness, pad roughness, abrasive size, and abrasive geometry into the same formulation to predict the material removal rate (MRR). An interface between the chemical effect and mechanical effect has been constructed through a fitting parameter H/sub w/ a "dynamical" hardness value of the wafer surface, in the model. It reflects the influences of chemicals on the mechanical material removal. The fluid effect in the current model is attributed to the number of active abrasives. It is found that the nonlinear down pressure dependence of material removal rate is related to a probability density function of the abrasive size and the elastic deformation of the pad. Compared with experimental results, the model accurately predicts MRR. With further verification of the model, a better understanding of the fundamental mechanism involved in material removal in the CMP process, particularly different roles played by the consumables and their interactions, can be obtained. read more read less

Topics:

Chemical-mechanical planarization (59%)59% related to the paper, Abrasive (53%)53% related to the paper, Surface finish (52%)52% related to the paper, Surface roughness (52%)52% related to the paper
544 Citations
Journal Article DOI: 10.1109/TSM.2010.2096437
Fifty Years of Moore's Law

Abstract:

The 1959 invention of the planar silicon transistor led to the development of the integrated circuit (IC) and the growth trend in IC complexity known as Moore's Law While Moore's observation came in 1965, his original trend line showing a doubling of components per chip each year began with one component in 1959 Thus, we have... The 1959 invention of the planar silicon transistor led to the development of the integrated circuit (IC) and the growth trend in IC complexity known as Moore's Law While Moore's observation came in 1965, his original trend line showing a doubling of components per chip each year began with one component in 1959 Thus, we have now experienced 50 years of Moore's Law This paper provides a history of Moore's Law through its many changes and reinterpretations, containing possibly a few new ones as well read more read less

Topics:

Moore's law (58%)58% related to the paper
412 Citations
Journal Article DOI: 10.1109/66.311341
Efficient scheduling policies to reduce mean and variance of cycle-time in semiconductor manufacturing plants
S.C.H. Lu1, D. Ramaswamy1, P.R. Kumar1

Abstract:

The problem of reducing the mean and variance of cycle time in semiconductor manufacturing plants is addressed. Such plants feature a characteristic reentrant process flow, where lots repeatedly return at different stages of their production to the same service stations for further processing, consequently creating much compe... The problem of reducing the mean and variance of cycle time in semiconductor manufacturing plants is addressed. Such plants feature a characteristic reentrant process flow, where lots repeatedly return at different stages of their production to the same service stations for further processing, consequently creating much competition for machines. We introduce a new class of scheduling policies, called Fluctuation Smoothing policies. Unanimously, our policies achieved the best mean cycle time and Standard Deviation of Cycle Time, in all the configurations of plant models and release policies tested. As an example, under the recommended Workload Regulation Release policy, for a heavily loaded Research and Development Fabrication Line model, our Fluctuation Smoothing policies achieved a reduction of 22.4% in the Mean Queueing Time, and a reduction of 52.0% in the Standard Deviation of Cycle Time, over the baseline FIFO policy. These conclusions are based on extensive simulations conducted on two models of semiconductor manufacturing plants. The first is a model of a Research and Development Fabrication Line. The second is an aggregate model intended to approximate a full scale production line. Statistical tests are used to corroborate our conclusions. > read more read less

Topics:

Scheduling (production processes) (54%)54% related to the paper, Standard deviation (52%)52% related to the paper
401 Citations
Journal Article DOI: 10.1109/TSM.2007.907607
Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes
Qinghua He1, Jin Wang

Abstract:

It has been recognized that effective fault detection techniques can help semiconductor manufacturers reduce scrap, increase equipment uptime, and reduce the usage of test wafers. Traditional univariate statistical process control charts have long been used for fault detection. Recently, multivariate statistical fault detecti... It has been recognized that effective fault detection techniques can help semiconductor manufacturers reduce scrap, increase equipment uptime, and reduce the usage of test wafers. Traditional univariate statistical process control charts have long been used for fault detection. Recently, multivariate statistical fault detection methods such as principal component analysis (PCA)-based methods have drawn increasing interest in the semiconductor manufacturing industry. However, the unique characteristics of the semiconductor processes, such as nonlinearity in most batch processes, multimodal batch trajectories due to product mix, and process steps with variable durations, have posed some difficulties to the PCA-based methods. To explicitly account for these unique characteristics, a fault detection method using the k-nearest neighbor rule (FD-kNN) is developed in this paper. Because in fault detection faults are usually not identified and characterized beforehand, in this paper the traditional kNN algorithm is adapted such that only normal operation data is needed. Because the developed method makes use of the kNN rule, which is a nonlinear classifier, it naturally handles possible nonlinearity in the data. Also, because the FD-kNN method makes decisions based on small local neighborhoods of similar batches, it is well suited for multimodal cases. Another feature of the proposed FD-kNN method, which is essential for online fault detection, is that the data preprocessing is performed automatically without human intervention. These capabilities of the developed FD-kNN method are demonstrated by simulated illustrative examples as well as an industrial example. read more read less

Topics:

Fault detection and isolation (64%)64% related to the paper, Fault coverage (62%)62% related to the paper, Statistical process control (53%)53% related to the paper, Data pre-processing (52%)52% related to the paper
391 Citations
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Frequently asked questions

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Yes, the template is compliant with the IEEE Transactions on Semiconductor Manufacturing guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in IEEE Transactions on Semiconductor Manufacturing?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the IEEE Transactions on Semiconductor Manufacturing citation style.

4. Can I use the IEEE Transactions on Semiconductor Manufacturing templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for IEEE Transactions on Semiconductor Manufacturing.

5. Can I use a manuscript in IEEE Transactions on Semiconductor Manufacturing that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper IEEE Transactions on Semiconductor Manufacturing that you can download at the end.

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Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

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SciSpace's IEEE Transactions on Semiconductor Manufacturing is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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After writing your paper autoformatting in IEEE Transactions on Semiconductor Manufacturing, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE Transactions on Semiconductor Manufacturing's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for IEEE Transactions on Semiconductor Manufacturing?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IEEE Transactions on Semiconductor Manufacturing. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In IEEE Transactions on Semiconductor Manufacturing?

The 5 most common citation types in order of usage for IEEE Transactions on Semiconductor Manufacturing are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the IEEE Transactions on Semiconductor Manufacturing?

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16. Can I download IEEE Transactions on Semiconductor Manufacturing in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in IEEE Transactions on Semiconductor Manufacturing Endnote style according to Elsevier guidelines.

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