Optimization Problems of Nanosized Semiconductor Heterostructures View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

K. K. Abgaryan

ABSTRACT

A new approach is presented that allows solving optimization problems of nanosized semiconductor heterostructures. We have formulated and solved the problem of determining the optimal doping of a barrier layer consisting of a number of sublayers, which provides a preset concentration of electrons in the conduction channel of semiconductor heterostructures. To solve the problem, effective optimization algorithms based on gradient methods are developed. As an example, an Al0.25GaN/GaN heterostructure with a total barrier layer thickness of 30 nm is considered. The results obtained in the numerical experiment are consistent with the modern trend towards the transition from a homogeneous doping profile to a planar δ-doping in field-effect transistor manufacturing technologies. The developed technique of mathematical simulation and optimization can be used in field-effect transistor manufacturing technologies. The approaches presented in the work create the conditions for the automated design of such structures. More... »

PAGES

583-588

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s1063739718080024

DOI

http://dx.doi.org/10.1134/s1063739718080024

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112898431


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Numerical and Computational Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Russian Academy of Sciences", 
          "id": "https://www.grid.ac/institutes/grid.4886.2", 
          "name": [
            "Dorodnitsyn Computing Center, Federal Research Center Informatics and Management,\nRussian Academy of Sciences, 119333, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Abgaryan", 
        "givenName": "K. K.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/pssc.201510159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009521560"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1134/s0965542516010048", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026106725", 
          "https://doi.org/10.1134/s0965542516010048"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0953-8984/14/13/302", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026972351"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/pssc.201400200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037577524"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1134/s1063782614050121", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046958467", 
          "https://doi.org/10.1134/s1063782614050121"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.365396", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057992570"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrev.140.a1133", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060431417"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrev.140.a1133", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060431417"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.54.11169", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060581262"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.54.11169", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060581262"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/b13776", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095906207"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "A new approach is presented that allows solving optimization problems of nanosized semiconductor heterostructures. We have formulated and solved the problem of determining the optimal doping of a barrier layer consisting of a number of sublayers, which provides a preset concentration of electrons in the conduction channel of semiconductor heterostructures. To solve the problem, effective optimization algorithms based on gradient methods are developed. As an example, an Al0.25GaN/GaN heterostructure with a total barrier layer thickness of 30 nm is considered. The results obtained in the numerical experiment are consistent with the modern trend towards the transition from a homogeneous doping profile to a planar \u03b4-doping in field-effect transistor manufacturing technologies. The developed technique of mathematical simulation and optimization can be used in field-effect transistor manufacturing technologies. The approaches presented in the work create the conditions for the automated design of such structures.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1134/s1063739718080024", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.6742768", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1136391", 
        "issn": [
          "1063-7397", 
          "1608-3415"
        ], 
        "name": "Russian Microelectronics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "8", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "47"
      }
    ], 
    "name": "Optimization Problems of Nanosized Semiconductor Heterostructures", 
    "pagination": "583-588", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "6bd298dbea38b2a3775583074b965ea700ea4a7eef3551c852feec6e36df0b0f"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1134/s1063739718080024"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112898431"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1134/s1063739718080024", 
      "https://app.dimensions.ai/details/publication/pub.1112898431"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:42", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000363_0000000363/records_70058_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1134%2FS1063739718080024"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1134/s1063739718080024'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1134/s1063739718080024'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1134/s1063739718080024'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1134/s1063739718080024'


 

This table displays all metadata directly associated to this object as RDF triples.

91 TRIPLES      21 PREDICATES      36 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1134/s1063739718080024 schema:about anzsrc-for:01
2 anzsrc-for:0103
3 schema:author Nc8a3df18609a42f6950d512fea70831c
4 schema:citation sg:pub.10.1134/s0965542516010048
5 sg:pub.10.1134/s1063782614050121
6 https://doi.org/10.1002/pssc.201400200
7 https://doi.org/10.1002/pssc.201510159
8 https://doi.org/10.1063/1.365396
9 https://doi.org/10.1088/0953-8984/14/13/302
10 https://doi.org/10.1103/physrev.140.a1133
11 https://doi.org/10.1103/physrevb.54.11169
12 https://doi.org/10.1201/b13776
13 schema:datePublished 2018-12
14 schema:datePublishedReg 2018-12-01
15 schema:description A new approach is presented that allows solving optimization problems of nanosized semiconductor heterostructures. We have formulated and solved the problem of determining the optimal doping of a barrier layer consisting of a number of sublayers, which provides a preset concentration of electrons in the conduction channel of semiconductor heterostructures. To solve the problem, effective optimization algorithms based on gradient methods are developed. As an example, an Al0.25GaN/GaN heterostructure with a total barrier layer thickness of 30 nm is considered. The results obtained in the numerical experiment are consistent with the modern trend towards the transition from a homogeneous doping profile to a planar δ-doping in field-effect transistor manufacturing technologies. The developed technique of mathematical simulation and optimization can be used in field-effect transistor manufacturing technologies. The approaches presented in the work create the conditions for the automated design of such structures.
16 schema:genre research_article
17 schema:inLanguage en
18 schema:isAccessibleForFree false
19 schema:isPartOf N61ebcccf63ed42d1983ad2ee4d87e0df
20 N8c33663ea79740ff840b79d801c59fa3
21 sg:journal.1136391
22 schema:name Optimization Problems of Nanosized Semiconductor Heterostructures
23 schema:pagination 583-588
24 schema:productId N1cae3e43e51c46998b1fdf1c5fb38bce
25 N4538e390bbda470dbde9b6fab8a50bbd
26 N4a815b7cad99437a95b278a39ea805d0
27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112898431
28 https://doi.org/10.1134/s1063739718080024
29 schema:sdDatePublished 2019-04-11T12:42
30 schema:sdLicense https://scigraph.springernature.com/explorer/license/
31 schema:sdPublisher N9c0159bc76a14166b7788d51ee88a10f
32 schema:url https://link.springer.com/10.1134%2FS1063739718080024
33 sgo:license sg:explorer/license/
34 sgo:sdDataset articles
35 rdf:type schema:ScholarlyArticle
36 N1cae3e43e51c46998b1fdf1c5fb38bce schema:name readcube_id
37 schema:value 6bd298dbea38b2a3775583074b965ea700ea4a7eef3551c852feec6e36df0b0f
38 rdf:type schema:PropertyValue
39 N32e4c48bb6a54c2c834d6378462374fb schema:affiliation https://www.grid.ac/institutes/grid.4886.2
40 schema:familyName Abgaryan
41 schema:givenName K. K.
42 rdf:type schema:Person
43 N4538e390bbda470dbde9b6fab8a50bbd schema:name dimensions_id
44 schema:value pub.1112898431
45 rdf:type schema:PropertyValue
46 N4a815b7cad99437a95b278a39ea805d0 schema:name doi
47 schema:value 10.1134/s1063739718080024
48 rdf:type schema:PropertyValue
49 N61ebcccf63ed42d1983ad2ee4d87e0df schema:issueNumber 8
50 rdf:type schema:PublicationIssue
51 N8c33663ea79740ff840b79d801c59fa3 schema:volumeNumber 47
52 rdf:type schema:PublicationVolume
53 N9c0159bc76a14166b7788d51ee88a10f schema:name Springer Nature - SN SciGraph project
54 rdf:type schema:Organization
55 Nc8a3df18609a42f6950d512fea70831c rdf:first N32e4c48bb6a54c2c834d6378462374fb
56 rdf:rest rdf:nil
57 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
58 schema:name Mathematical Sciences
59 rdf:type schema:DefinedTerm
60 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
61 schema:name Numerical and Computational Mathematics
62 rdf:type schema:DefinedTerm
63 sg:grant.6742768 http://pending.schema.org/fundedItem sg:pub.10.1134/s1063739718080024
64 rdf:type schema:MonetaryGrant
65 sg:journal.1136391 schema:issn 1063-7397
66 1608-3415
67 schema:name Russian Microelectronics
68 rdf:type schema:Periodical
69 sg:pub.10.1134/s0965542516010048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026106725
70 https://doi.org/10.1134/s0965542516010048
71 rdf:type schema:CreativeWork
72 sg:pub.10.1134/s1063782614050121 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046958467
73 https://doi.org/10.1134/s1063782614050121
74 rdf:type schema:CreativeWork
75 https://doi.org/10.1002/pssc.201400200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037577524
76 rdf:type schema:CreativeWork
77 https://doi.org/10.1002/pssc.201510159 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009521560
78 rdf:type schema:CreativeWork
79 https://doi.org/10.1063/1.365396 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057992570
80 rdf:type schema:CreativeWork
81 https://doi.org/10.1088/0953-8984/14/13/302 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026972351
82 rdf:type schema:CreativeWork
83 https://doi.org/10.1103/physrev.140.a1133 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060431417
84 rdf:type schema:CreativeWork
85 https://doi.org/10.1103/physrevb.54.11169 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060581262
86 rdf:type schema:CreativeWork
87 https://doi.org/10.1201/b13776 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095906207
88 rdf:type schema:CreativeWork
89 https://www.grid.ac/institutes/grid.4886.2 schema:alternateName Russian Academy of Sciences
90 schema:name Dorodnitsyn Computing Center, Federal Research Center Informatics and Management, Russian Academy of Sciences, 119333, Moscow, Russia
91 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...