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My Publishing to Date

May 1, 2018

 

 

 

 

 

As a researcher at Georgia Institute of Technology, I applied Artificial Neural Networks to Semiconductor Manufacturing and co-authored the following articles under Dr. Gary S. May. Dr. May went on to head the Electrical and Computer Engineering department at Tech, and then became Dean of College of Engineering.

 

In February, 2017 he was appointed the Chancellor of the University of California at Davis.

 

 

 

 

Google Scholars on Christopher Himmel

 

 

Advantages of plasma etch modeling using neural networks over statistical techniques

CD Himmel, GS May

IEEE Transactions on semiconductor manufacturing 6 (2), 103-111, 1993

 

Due to the inherent complexity of the plasma etch process, approaches to modeling this critical integrated circuit fabrication step have met with varying degrees of success. Recently, a new adaptive learning approach involving neural networks has been applied to the modeling of polysilicon film growth by low-pressure chemical vapor deposition (LPCVD). In this paper, neural network modeling is applied to the removal of polysilicon films by plasma etching. The plasma etch process under investigation was previously modeled using the empirical response surface approach. However, in comparing neural network methods with the statistical techniques, it is shown that the neural network models exhibit superior accuracy and require fewer training experiments. Furthermore, the results of this study indicate that the predictive capabilities of the neural models are superior to that of their statistical counterparts for the same experimental data

 

Cited 231 times

 

 

Time series modeling of reactive ion etching using neural networks

MD Baker, CD Himmel, GS May

IEEE Transactions on Semiconductor Manufacturing 8 (1), 62-71, 1995

 

Neural networks have been used to model the behavior of real-time tool data in a reactive ion etch (RIE) process. An etch monitoring and data acquisition system for transferring data from the RIE chamber to a remote workstation was designed and implemented on a Plasma Therm Series 700 Dual Chamber etcher. This system monitors gas flow rates, RF power, temperature, pressure, and dc bias voltage. A neural network was trained on the monitored data using the feed-forward, error backpropagation algorithm. This network was used to perform three distinct modeling tasks. First, the network was trained on a subset of ten samples of the time series representing a single process run, and subsequently used to forecast the next data point. In the second task, the network was trained as in the first task, but used to predict the next ten values of the data sequence. In each of the first two tasks, the trained network yielded errors of less than 5%. In the final task, a neural net was used to generate a malfunction alarm when the sampled data did not conform to its previously established pattern

 

Cited 63 times

 

 

A comparison of statistically-based and neural network models of plasma etch behavior

CD Himmel, B Kim, GS May

Semiconductor Manufacturing Science Symposium, 1992. ISMSS 1992., IEEE/SEMI, 1992

 

A neural network modeling methodology is applied to the removal of polysilicon films by plasma etching. For a polysilicon etch in a CCl<sub>4</sub>/He/O<sub>4</sub> plasma, the etch rate, uniformity, and selectivity to both silicon dioxide and photoresist were modeled as a function of RF power, pressure, electrode spacing, and the three gas flows. Neural process models were subsequently compared to models derived by response surface methodology (RSM) for the same data. It was demonstrated that the neural models possess significantly superior performance. Furthermore, the derivation of accurate neural models was shown to require fewer training experiments. As a result, neural network modeling promises to be a faster, more efficient, and less expensive method of process characterization

 

Cited 33 times

 

 

In-situ prediction of reactive ion etch endpoint using neural networks

MD Baker, CD Himmel, GS May

IEEE Transactions on Components, Packaging, and Manufacturing Technology, 1995

 

Reactive ion etching (RIE) in radio frequency glow discharges is perhaps the most popular means of achieving the level of detail necessary to pattern small geometry features in electronics manufacturing. However, the complexity of the RIE process has prompted the use of empirical models utilizing neural networks, which offer advantages in both accuracy and robustness over statistical methods. In this paper, a neural network trained to model the correlation between DC bias and etch rate was used to predict the time required to remove a specified thickness of silicon dioxide (SiO<sub>2</sub>) in a CHF<sub>3 </sub>/O<sub>2</sub> plasma. A real-time data acquisition system that transmits process conditions from a Plasma Therm 700 series RIE system was used to monitor DC bias during etching. A back-propagation neural network was trained to predict the amount of time required to etch the remaining amount of film while in the midst of etching. Inputs to the network included elapsed time during the etch run, the desired etch depth, gas flow rates, chamber pressure, and RF power. This network exhibited a 26 s RMS error on training data, and predicted the process endpoint on a set of test etch recipes with an average error of less than two minutes for a process time of about 25 min

 

Cited 32 times

 

 

Real-time predictive control of semiconductor manufacturing processes using neural networks

CD Himmel, TS Kim, A Krauss, EW Kamen, GS May

American Control Conference, Proceedings of the 1995 2, 1240-1244, 1995

 

As a result of consistent demands on semiconductor manufacturers to produce circuits with increased density and complexity, stringent process control has become an issue of growing importance in this industry. Earlier work has shown that neural networks offer great promise in modeling complex fabrication processes such as reactive ion etching (RIE). Motivated by these results, this paper explores the use of neural networks for real-time, model-based control of semiconductor manufacturing processes. This objective is accomplished in part by constructing a q-step ahead predictive model for the system, which can be inverted (or approximately inverted) to achieve the desired control. The efficacy of this approach is demonstrated: (1) using a process simulated by a nonlinear equation; (2) using experimental input/output data from an actual RIE process to examine run-by-run control; and (3) by performing real-time, one-step ahead predictive control of a dynamic process which reflects typical RIE behavior

 

Cited 16 times

 

 

In-situ prediction of RIE time to completion using neural networks

D Baker, CD Himmel, GS May

Electronics Manufacturing Technology Symposium, 1994. Low-Cost Manufacturing, 1994

 

Reactive ion etching (RIE) in radio frequency glow discharges is perhaps the most popular means of achieving the level of detail necessary to pattern small geometry features in electronics manufacturing. However, the complexity of the RIE process has prompted the use of empirical models utilizing neural networks, which offer advantages in both accuracy and robustness over statistical methods. In this paper, a neural network trained to model the correlation between DC bias and etch rate was used to predict the time required to remove a specified thickness of silicon dioxide (SiO<sub>2</sub>) in a CHF<sub>3 </sub>/O<sub>2</sub> plasma. A real-time data acquisition system that transmits process conditions from a Plasma Therm 700 series RIE system was used to monitor DC bias during etching. A back-propagation neural network was trained to predict the amount of time required to etch the remaining amount of film while in the midst of etching. Inputs to the network included elapsed time during the etch run, the desired etch depth, gas flow rates, chamber pressure, and RF power. This network exhibited a 26-second RMS error on training data, and predicted the process endpoint on a set of test etch recipes with an average error of less than two minutes for a process time of about 25 minutes

 

Cited 1 time

 

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