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Long-Term Memory01:18

Long-Term Memory

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Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
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There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
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Intrinsic semiconductors are highly pure materials with no impurities. At absolute zero, these semiconductors behave as perfect insulators because all the valence electrons are bound, and the conduction band is empty, disallowing electrical conduction. The Fermi level is a concept used to describe the probability of occupancy of energy levels by electrons at thermal equilibrium. In intrinsic semiconductors, the Fermi level is positioned at the midpoint of the energy gap at absolute zero. When...
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The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
Schottky Barriers
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Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory.

Tze Chiang Tin1,2, Kang Leng Chiew1, Siew Chee Phang2

  • 1Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.

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This study introduces a machine learning model using LSTM for accurate forecasting of incoming semiconductor fabrication work-in-progress. This improves scheduling of preventive maintenance, reducing operational downtime and increasing efficiency.

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Area of Science:

  • Semiconductor Manufacturing
  • Operations Research
  • Machine Learning

Background:

  • Preventive maintenance is essential for tool reliability in semiconductor fabrication but causes significant downtime.
  • Current forecasting methods for incoming work-in-progress (WIP) lack accuracy due to inability to capture time-dependent behavior.
  • Long maintenance downtimes increase semiconductor fabrication cycle times, impacting overall efficiency.

Purpose of the Study:

  • To develop an accurate forecasting model for incoming WIP to semiconductor fabrication equipment.
  • To optimize the scheduling of preventive maintenance activities by improving WIP prediction.
  • To reduce the cycle time and enhance the operational efficiency of semiconductor fabrication foundries.

Main Methods:

  • Utilized a Long Short-Term Memory (LSTM) network, a type of recurrent neural network, for time-series forecasting.
  • Developed a multistep-ahead prediction model for incoming WIP to equipment groups.
  • Compared the proposed LSTM model's performance against the existing statistical forecasting method used in the fabrication foundry.

Main Results:

  • The LSTM-based forecasting model demonstrated superior performance compared to the traditional statistical method.
  • The proposed model achieved a higher hit rate in predicting incoming WIP.
  • The model showed a better Pearson's correlation coefficient (r), indicating improved prediction accuracy.

Conclusions:

  • Machine learning, specifically LSTM, offers a more accurate approach to forecasting WIP in semiconductor fabrication.
  • Improved WIP forecasting enables better planning of preventive maintenance, minimizing disruption.
  • The proposed model enhances operational reliability and efficiency in semiconductor manufacturing environments.