Production-Level Artificial Intelligence Applications in Semiconductor Supply Chains

Abstract : This is a panel paper which discusses the use of Artificial Intelligence (AI) techniques to address production level problems in semiconductor manufacturing. We have gathered a group of expert semiconductor researchers and practitioners from around the world who have applied AI techniques to semiconductor problems and the paper provides their answers to an initial set of questions. These serve to provide a description of the AI work that has taken place already and to make suggestions for future directions in this arena.

 EXISTING SYSTEM :

 AI models analyze historical sales data, market trends, and economic indicators to predict future demand. Techniques like time-series analysis, regression models, and neural networks are commonly used. Leveraging big data from various sources (e.g., customer orders, market research) helps improve the accuracy of demand forecasts. AI predicts inventory requirements based on real-time data, reducing both overstock and stockouts. These systems automatically order materials based on AI predictions to maintain optimal inventory levels.

 DISADVANTAGE :

 Implementing AI solutions requires significant upfront investment in technology, infrastructure, and skilled personnel. Developing and training AI models is resource-intensive and requires ongoing investment to keep the systems updated and effective Many semiconductor companies have legacy systems that may not be compatible with new AI technologies, requiring complex and costly integration efforts.

 PROPOSED SYSTEM :

 Conduct a thorough assessment of current supply chain processes to identify areas where AI can add value. Develop a strategic plan outlining the objectives, scope, and expected outcomes of AI implementation. Include a clear timeline and resource allocation. Identify and collect relevant data from various sources, including manufacturing data, supply chain logistics, sales forecasts, and market trends. Implement processes to ensure data accuracy, consistency, and completeness. Use data cleaning and preprocessing techniques.

 ADVANTAGE :

 Choose appropriate AI models for different applications (e.g., machine learning for demand forecasting, NLP for supplier management). Develop custom algorithms tailored to the specific needs of the semiconductor supply chain. Train AI models using historical data and validate their performance with testing datasets. Employ cross-validation and other techniques to ensure robustness.

quantum-computing Neural-Tangent-Kernels-(NTKs) Quantum-algorithms- Quantum-Path-Kernel classical-deep-neural-networks-

Remove-noise- semi-supervised-learning Deep-learning- machine-learning- GlobalNet

LSTM Machine-learning-(ML) Long-Short-Term-Memory-(LSTM) Random-Forest-Regressor-model Recurrent-Neural-Network

SSL-methods EEG-signals-and-brain-function- EEG-pre-processing- band-pass-filtering SSL-framework-

NLP-for-supplier-management AI-models supply-chain-logistics time-series-analysis AI-predicts