Parametric manufacturing yield modeling of GaAs/AlGaAs multiple quantum well avalanche photodiodes

Ilgu Yun; Gary S. May

doi:10.1109/66.762882

Parametric manufacturing yield modeling of GaAs/AlGaAs multiple quantum well avalanche photodiodes

Ilgu Yun, Gary S. May

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

Abstract

Described is a systematic methodology for modelling the parametric performance of GaAs multiple quantum well (MQW) avalanche photodiodes (APDs). Through application to MQW APDs, it is shown that using a small number of test devices with varying active diameters, barrier and well widths, and doping concentrations enables prediction of the expected performance variation of APD gain and noise in larger population of devices. The method compares favorably with Monte Carlo techniques and allows device yield prediction prior to high volume manufacturing in order to evaluate the impact of both design decisions and process capability.

Original language	English
Pages (from-to)	238-251
Number of pages	14
Journal	IEEE Transactions on Semiconductor Manufacturing
Volume	12
Issue number	2
DOIs	https://doi.org/10.1109/66.762882
Publication status	Published - 1999
Event	Proceedings of the 1998 ICMTS - Kanazawa, Jpn Duration: 1998 Mar 23 → 1998 Mar 26

Bibliographical note

Funding Information:
Manuscript received November 4, 1997; revised November 10, 1998. This work was supported by NASA under Contract NAGW-2753 and NSF under Grant DDM-9 358 163. The authors are with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250 USA. Publisher Item Identifier S 0894-6507(99)03775-6.

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/66.762882

Cite this

@article{3d472caff43546e5ba8156837e51eec4,

title = "Parametric manufacturing yield modeling of GaAs/AlGaAs multiple quantum well avalanche photodiodes",

abstract = "Described is a systematic methodology for modelling the parametric performance of GaAs multiple quantum well (MQW) avalanche photodiodes (APDs). Through application to MQW APDs, it is shown that using a small number of test devices with varying active diameters, barrier and well widths, and doping concentrations enables prediction of the expected performance variation of APD gain and noise in larger population of devices. The method compares favorably with Monte Carlo techniques and allows device yield prediction prior to high volume manufacturing in order to evaluate the impact of both design decisions and process capability.",

author = "Ilgu Yun and May, {Gary S.}",

note = "Funding Information: Manuscript received November 4, 1997; revised November 10, 1998. This work was supported by NASA under Contract NAGW-2753 and NSF under Grant DDM-9 358 163. The authors are with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250 USA. Publisher Item Identifier S 0894-6507(99)03775-6.; Proceedings of the 1998 ICMTS ; Conference date: 23-03-1998 Through 26-03-1998",

year = "1999",

doi = "10.1109/66.762882",

language = "English",

volume = "12",

pages = "238--251",

journal = "IEEE Transactions on Semiconductor Manufacturing",

issn = "0894-6507",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "2",

}

TY - JOUR

T1 - Parametric manufacturing yield modeling of GaAs/AlGaAs multiple quantum well avalanche photodiodes

AU - Yun, Ilgu

AU - May, Gary S.

N1 - Funding Information: Manuscript received November 4, 1997; revised November 10, 1998. This work was supported by NASA under Contract NAGW-2753 and NSF under Grant DDM-9 358 163. The authors are with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250 USA. Publisher Item Identifier S 0894-6507(99)03775-6.

PY - 1999

Y1 - 1999

N2 - Described is a systematic methodology for modelling the parametric performance of GaAs multiple quantum well (MQW) avalanche photodiodes (APDs). Through application to MQW APDs, it is shown that using a small number of test devices with varying active diameters, barrier and well widths, and doping concentrations enables prediction of the expected performance variation of APD gain and noise in larger population of devices. The method compares favorably with Monte Carlo techniques and allows device yield prediction prior to high volume manufacturing in order to evaluate the impact of both design decisions and process capability.

AB - Described is a systematic methodology for modelling the parametric performance of GaAs multiple quantum well (MQW) avalanche photodiodes (APDs). Through application to MQW APDs, it is shown that using a small number of test devices with varying active diameters, barrier and well widths, and doping concentrations enables prediction of the expected performance variation of APD gain and noise in larger population of devices. The method compares favorably with Monte Carlo techniques and allows device yield prediction prior to high volume manufacturing in order to evaluate the impact of both design decisions and process capability.

UR - http://www.scopus.com/inward/record.url?scp=0033360476&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033360476&partnerID=8YFLogxK

U2 - 10.1109/66.762882

DO - 10.1109/66.762882

M3 - Conference article

AN - SCOPUS:0033360476

SN - 0894-6507

VL - 12

SP - 238

EP - 251

JO - IEEE Transactions on Semiconductor Manufacturing

JF - IEEE Transactions on Semiconductor Manufacturing

IS - 2

T2 - Proceedings of the 1998 ICMTS

Y2 - 23 March 1998 through 26 March 1998

ER -

Parametric manufacturing yield modeling of GaAs/AlGaAs multiple quantum well avalanche photodiodes

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this