### Abstract

This paper presents a novel methodology for the parametric yield prediction of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APDs). Even in a defect-free manufacturing environment, random variations in the APD fabrication process lead to varying levels of device performance. Accurate performance prediction requires precise characterization of these variations. The approach described herein requires a model of the probability distribution of each of the relevant process variables, as well as a model to account for the correlation between this measured process data and device performance metrics. Neural networks are proposed as a tool for generating these models, which enable the computation of the joint density function required for predicting performance using Jacobian transformation method. The resulting density function can then be numerically integrated to determine parametric yield. In apply this methodology to MQW APDs, using a small number of test devices enables accurate prediction of the expected performance variation of APD gain and noise in larger populations of devices. This approach potentially allows yield estimation prior to high volume manufacturing.

Original language | English |
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Pages | 105-112 |

Number of pages | 8 |

Publication status | Published - 1997 Dec 1 |

Event | Proceedings of the 1997 21st IEEE/CPMT International Electronics Manufacturing Technology (IEMT) Symposium - Austin, TX, USA Duration: 1997 Oct 13 → 1997 Oct 15 |

### Other

Other | Proceedings of the 1997 21st IEEE/CPMT International Electronics Manufacturing Technology (IEMT) Symposium |
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City | Austin, TX, USA |

Period | 97/10/13 → 97/10/15 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Electrical and Electronic Engineering

### Cite this

*Evaluating the manufacturability of GaAs/AlGaAs multiple quantum well avalanche photodiodes using neural networks*. 105-112. Paper presented at Proceedings of the 1997 21st IEEE/CPMT International Electronics Manufacturing Technology (IEMT) Symposium, Austin, TX, USA, .

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**Evaluating the manufacturability of GaAs/AlGaAs multiple quantum well avalanche photodiodes using neural networks.** / Yun, Ilgu; May, Gary S.

Research output: Contribution to conference › Paper

TY - CONF

T1 - Evaluating the manufacturability of GaAs/AlGaAs multiple quantum well avalanche photodiodes using neural networks

AU - Yun, Ilgu

AU - May, Gary S.

PY - 1997/12/1

Y1 - 1997/12/1

N2 - This paper presents a novel methodology for the parametric yield prediction of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APDs). Even in a defect-free manufacturing environment, random variations in the APD fabrication process lead to varying levels of device performance. Accurate performance prediction requires precise characterization of these variations. The approach described herein requires a model of the probability distribution of each of the relevant process variables, as well as a model to account for the correlation between this measured process data and device performance metrics. Neural networks are proposed as a tool for generating these models, which enable the computation of the joint density function required for predicting performance using Jacobian transformation method. The resulting density function can then be numerically integrated to determine parametric yield. In apply this methodology to MQW APDs, using a small number of test devices enables accurate prediction of the expected performance variation of APD gain and noise in larger populations of devices. This approach potentially allows yield estimation prior to high volume manufacturing.

AB - This paper presents a novel methodology for the parametric yield prediction of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APDs). Even in a defect-free manufacturing environment, random variations in the APD fabrication process lead to varying levels of device performance. Accurate performance prediction requires precise characterization of these variations. The approach described herein requires a model of the probability distribution of each of the relevant process variables, as well as a model to account for the correlation between this measured process data and device performance metrics. Neural networks are proposed as a tool for generating these models, which enable the computation of the joint density function required for predicting performance using Jacobian transformation method. The resulting density function can then be numerically integrated to determine parametric yield. In apply this methodology to MQW APDs, using a small number of test devices enables accurate prediction of the expected performance variation of APD gain and noise in larger populations of devices. This approach potentially allows yield estimation prior to high volume manufacturing.

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M3 - Paper

AN - SCOPUS:0031352414

SP - 105

EP - 112

ER -