Multiobjective scheduling for semiconductor manufacturing plants

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment

Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learning

Semiconductor manufacturing is a notoriously complex and costly multi-step process involving a long sequence of operations on expensive and quantity-limited equipment. Recent chip shortages and their impacts have highlighted the importance of semiconductors in the global supply chains and how reliant on those our daily lives are. Due to the investment cost, environmental impact, and time scale needed to build new factories, it is difficult to ramp up production when demand spikes. This work introduces a method to successfully learn to schedule a semiconductor manufacturing facility more efficiently using deep reinforcement and self-supervised learning. We propose the first adaptive scheduling approach to handle complex, continuous, stochastic, dynamic, modern semiconductor manufacturing models. Our method outperforms the traditional hierarchical dispatching strategies typically used in semiconductor manufacturing plants, substantially reducing each order's tardiness and time until completion. As a result, our method yields a better allocation of resources in the semiconductor manufacturing process

Some aspects of process control in semiconductor manufacturing

This paper outlines some aspects of process control in semiconductor manufacturing. Starting with an outline of the semiconductor manufacturing process, the contribution will discuss temperature control of the chemical vapour deposition stage and the control of the wafer etching process, based on the industrial experience of the first two authors. Subsequently, the authors draw the attention of the semiconductor manufacturing community to the potential of properly tuned PID controllers for the achievement of simple and high performance control solutions

Risk Mitigation Of Outsourcing Manufacturing Process: A Study On The Semiconductor Manufacturing Organizations In Malaysia

Penggunaan perkhidmatan pihak ketiga daripada proses pembuatan semikonduktor menjadi sebahagian daripada strategi korporat sebuah organisasi yang didorong oleh kelebihan kos dan fleksibiliti dalam ketidakpastian. Outsourcing of semiconductor manufacturing process is becoming integral part of the corporate strategy of an organization which is driven by cost advantage and flexibility during uncertainty

Data Engineering for the Analysis of Semiconductor Manufacturing Data

We have analyzed manufacturing data from several different semiconductor manufacturing plants, using decision tree induction software called Q-YIELD. The software generates rules for predicting when a given product should be rejected. The rules are intended to help the process engineers improve the yield of the product, by helping them to discover the causes of rejection. Experience with Q-YIELD has taught us the importance of data engineering -- preprocessing the data to enable or facilitate decision tree induction. This paper discusses some of the data engineering problems we have encountered with semiconductor manufacturing data. The paper deals with two broad classes of problems: engineering the features in a feature vector representation and engineering the definition of the target concept (the classes). Manufacturing process data present special problems for feature engineering, since the data have multiple levels of granularity (detail, resolution). Engineering the target concept is important, due to our focus on understanding the past, as opposed to the more common focus in machine learning on predicting the future

Semiconductor manufacturing simulation design and analysis with limited data

This paper discusses simulation design and analysis for Silicon Carbide (SiC) manufacturing operations management at New York Power Electronics Manufacturing Consortium (PEMC) facility. Prior work has addressed the development of manufacturing system simulation as the decision support to solve the strategic equipment portfolio selection problem for the SiC fab design [1]. As we move into the phase of collecting data from the equipment purchased for the PEMC facility, we discuss how to redesign our manufacturing simulations and analyze their outputs to overcome the challenges that naturally arise in the presence of limited fab data. We conclude with insights on how an approach aimed to reflect learning from data can enable our discrete-event stochastic simulation to accurately estimate the performance measures for SiC manufacturing at the PEMC facility

Photon assisted tunneling in pairs of silicon donors

Shallow donors in silicon are favorable candidates for the implementation of solid-state quantum computer architectures because of the promising combination of atomiclike coherence properties and scalability from the semiconductor manufacturing industry. Quantum processing schemes require (among other things) controlled information transfer for readout. Here we demonstrate controlled electron tunneling at 10 K from P to Sb impurities and vice versa with the assistance of resonant terahertz photons