Bio-inspired Multiple Neural Networks Based Process

Profile Category

Faulty OK

Profile Category

OK OK

Faulty Faulty

Figure 12: Example of profiles extraction after via centring process (left). Example of profiles to category association during the learning phase (right).

Figure 13: Profiles extraction for size and localization of the probe mark (left). Experimental result showing a fault detection and its localization in the via (right).

All vias of a tested wafer are inspected and analysed. At the end of the process, the system shows a wafer map which presents the results and statistics on the probe quality and its alignment with the wafer. All the defects are memorized in a log file. In summary, the detection and classification tasks of our PMI application are done in three steps:

via localization in the acquired image, mark size estimation and probe impact classification (good, bad or none).

The method, which was retained, is based on profiles analysis using kennel functions based ANN.

Each extracted profile of the image (using a square shape, figures 12 and 13) is compared to a reference learned database in which each profile is associated with its appropriated category. Different categories, related to different needed features (as: size, functional signature, etc).

Experiments on different kinds of chips and on various probe defects have proven the efficiency of the neural approach to this kind of perception problem. The developed intelligent PMI system outperformed the best solutions offered by competitors by 30%: the best response time per via obtained using other wafer probers was about 600 ms and our neural based system analyzes one via every 400 ms, 300 of which were taken for the mechanical movements. Measures showed that the defect recognition neural module’s execution time

was negligible compared to the time spent for mechanical movements, as well as for the image acquisition (a ratio of 12 to 1 on any via). This application is presently inserted on a high throughput production line.

3.3 Bio-inspired Multiple Neural Networks Based Process Identification

The identification task involves two essential steps:

structure selection and parameter estimation. These two steps are linked and generally have to be realized in order to achieve the best compromise between error minimization and the total number of parameters in the final global model. In real world applications (situations), strong linearity, large number of related parameters and data nature complexity make the realization of those steps challenging, and so, the identification task difficult.

To overcome mentioned difficulties, one of the key points on which one can act is the complexity reduction. It may concern not only the problem representation level (data) but also may appear at processing procedure level. An issue could be model complexity reduction by splitting a complex problem into a set of simpler problems: multi-modelling where a set of simple models is used to

sculpt a complex behaviour (Murray, 1997). On this basis and inspired from animal brain structure (left picture of figure 14, showing the left side of a bird’s brain scheme and it’s auditory and motor pathways involved in the recognition and the production of song), we have designed an ANN based data driven treelike Multiple Model generator, that we called T-DTS (Treelike Divide To Simplify).This data driven neural networks based Multiple Processing (multiple model) structure is able to reduce complexity on both data and processing levels (Madani, 2003, b) (right picture of figure 14). T-DTS and associated algorithm construct a treelike evolutionary neural architecture automatically where nodes, called also

" Supervisor/Scheduler Units" (SU) are decision units and leafs called also " Neural Network based Models" (NNM) correspond to neural based processing units.

The T-DTS includes two main operation modes.

The first is the learning phase, when T-DTS system decomposes the input data and provides processing sub-structures and tools for decomposed sets of data.

The second phase is the operation phase (usage the system to process unlearned data). There could be also a pre-processing phase at the beginning, which arranges (prepare) data to be processed. Pre-processing phase could include several steps (conventional or neural stages). The learning phase is an important phase during which T-DTS performs several key operations: splitting the learning database into several sub-databases, constructing (dynamically) a treelike Supervision/Scheduling Unit (SSU) and building a set of sub-models (NNM) corresponding to each sub-database. Figure 15 represents the division and NNM construction bloc diagrams. As shown in this figure, if a neural based model cannot be built for an obtained sub-database, then, a new decomposition will be performed dividing the concerned sub-space into several other sub-spaces.

The second operation mode corresponds to the use of the constructed neural based Multi-model

system for processing unlearned (work) data. The SSU, constructed during the learning phase, receives data and classifies that data (pattern). Then, the most appropriated NNM is authorized (activated) to process that pattern.

Figure 14: Left side of a bird’s brain scheme and it’s auditory and motor pathways involved in the recognition and the production of song (left) and inspired T-DTS multiple model generator's general bloc-diagram (right).

NNM NNM SU

Pahth Generation Steps

SU Models Generation Time

NNM

NNM NNM NNM

NNM

Number of Generated Models

N 0

Figure 15: General bloc diagram of T-DTS learning phase,

We have applied T-DTS based Identifier to a real world industrial process identification and control problem. The process is a drilling rubber process used in plastic manufacturing industry.

Several non-linear parameters influence the manufacturing process. To perform an efficient control of the manufacturing quality (process quality), one should identify the global process (Chebira, 2003).

Controller Process

T-DTS based

Conventional Feedback Loop splitting and NNM generation process.

Figure 16: Industrial processing loop bloc diagram.

Figure 17: Process identification results showing the

A Kohonen SOM based SU with a 4x3 grid generates and supervises 12 NNM trained from learning database. Figures 16 and 17 show the bloc diagram of industrial processing loop and the identification result in the working phase, respectively. One can conclude that the predicted output is in accord with the measured one, obtained from the real plant.

4 CONCLUSION

Advances accomplished during last decades in Artificial Neural Networks area and issued techniques made possible to approach solution of a large number of difficult problems related to optimization, modeling, decision making, classification, data mining or nonlinear functions (behavior) approximation. Inspired from biological nervous systems and brain structure, these models take advantage from their learning and generalization capabilities, overcoming difficulties and limitations related to conventional techniques.

Today, conjunction of these new techniques with recent computational technologies offers attractive potential for designing and implementation of real-time intelligent industrial solutions. The main goal of the present paper was focused on ANN based techniques and their application to solve real-world and industrial problems. Of course, the presented models and applications don’t give an exhaustive state of art concerning huge potential offered by such approaches, but they could give, through above-presented ANN based applications, a good idea of promising capabilities of ANN based solutions to solve difficult future industrial changes.

ACKNOWLEDGEMENTS

Reported works were sponsored and supported by several research projects and industrial partners among which, French Ministry of Education, French Ministry of Research and IBM-France Company.

Author whish thank especially Dr. P. Tanhoff, and Dr. G. Detremiolles from IBM-France for their partnership and joint collaboration. I would also acknowledge Dr. V. Amarger, Dr. A. Chebira and Dr. A. Chohra from my research team (I²S Lab.) involved in presented works. I would thank Dr. G.

Mercier from PARIS XII University, who worked with me, during his Ph.D. and during several years in my lab, on intelligent adaptive control dilemma.

Finally, I would express my gratitude to Mr. M.

Rybnik, my Ph.D. student, working on aspects related to T-DTS, for useful discussions concerning the last part of this paper.

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