Use of neural networks in modeling semiconductor manufacturing processes: an example for plasma etch modeling

Rietman, Edward A.; Lory, Earl R.

doi:10.1109/66.267644

Cited by 69 publications

(12 citation statements)

References 9 publications

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“…Determining the optimal architecture to reduce network complexity is usually done by starting with a small network and increasing the number of hidden nodes or connections (cf., Weiss and Kulokowski [21]; Smith [22]; Rietman and Lory [23] and Marwah [24]). This tedious procedure does result in neural networks with good generalization ability (cf., Rietman [25]).…”

Section: Whole System Model For Feedback Controlmentioning

confidence: 98%

Pre-production results demonstrating multiple-system models for yield analysis

Rietman¹,

Friedman²,

Lory³

1997

IEEE Trans. Semicond. Manufact.

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We have assembled an integrated view of the entire via manufacturing process. This integrated study includes five key plasma processes that culminate in the production of vias on CMOS wafers. There are essentially no linear cross-correlations between the processing steps and there are no linear correlations between the individual process steps and the yield for vias, as measured by the resistance between metal-one (M1) and metaltwo (M2).Using a neural network, we demonstrate that the key processing steps to determine the M1M2 resistance are the thick oxide deposition and the anisotropic via etch. Of lesser significance are the etchback planarization, an isotropic etch and plasma enhanced tetra-ethoxy siliane (PETEOS) deposition. Keeping in mind that there are five processing steps, the numerical value of M1M2 resistance can be predicted ahead of time, before completion of all five processes. This prediction can be done to an accuracy of about 1 : By using adaptive neural networks, the intelligent agents can modify their predictive behavior with respect to process changes effected by the engineering staff. Our pre-production demonstration suggests that these programs could be used in feedback and feedforward control for production yield.

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Section: Whole System Model For Feedback Controlmentioning

confidence: 98%

Pre-production results demonstrating multiple-system models for yield analysis

Rietman¹,

Friedman²,

Lory³

1997

IEEE Trans. Semicond. Manufact.

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“…ANN has the capability to learn relationship complex relationship from training examples without task-specific programming. There have been number of reports of success case from various process, for example coil baking process [5], plasma etching [6], and superplastic forming process [7]. This paper describes an application of ANN to model weaving process parameter in order to predict process yield.…”

Section: Figure 2 Security Mesh Productionmentioning

confidence: 99%

Security wire mesh weaving process modelling with artificial neural network

Wongwan

Laosiritaworn

2018

MATEC Web Conf.

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This paper investigates weaving process in the production of security woven wire mesh. Weaving is a critical process of the entire production as the quality of the final product depends very much on this process. High defect rate and low production yield is now a major concern in the production. There has been no prior study of the relationship among variables such as inspection data and machine setting on production yield. Conducting experiments to investigate this relationship is not reasonable in this case, as the product targeted at premium market and scrap cost is very high. In order to investigate the effect of these parameters, artificial neural network (ANN) was applied to model the process with data from the company databases. The type of ANN used in this research was the multi-layer neural network trained with back-propagation algorithm. The results suggested that ANN can effectively be used to predict weaving process production yield. The use of ANN proposed in this research is not limit to only weaving process, but can be applied to other manufacturing process.

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“…Second, sophisticated models can be built off-Lline and implemented on-line so as to provide real-time approxihation or prediction of the critical variables. For this purpose, neural networks are instrumental and widely used in model-qased monitoring of etch processes [ 5 ] , [ 151, [18], [20], [21].…”

Section: Monitoringmentioning

confidence: 99%