A batching and scheduling algorithm for the diffusion area in semiconductor manufacturing

The impact of neighbourhood size on the process of simulated annealing: Computational experiments on the flowshop scheduling problem

In this paper we consider permutation flow shop scheduling problems with batch setup times. Each job has to be processed on each machine once and the technological routes are identical for all jobs. The set of jobs is divided into groups. There are given processing timest ij of jobi on machinej and setup timess rj on machinej when a job of ther-th group is processed after a job of another group. It is assumed that the same job order has to be chosen on each machine. We consider both the problems of minimizing the makespan and of minimizing the sum of completion times, where batch or item availability of the jobs is assumed. For these problems we give various constructive and iterative algorithms. The constructive algorithms are based on insertion techniques combined with beam search. We introduce suitable neighbourhood structures for such problems with batch setup times and describe iterative algorithms that are based on local search and reinsertion techniques. The developed algorithms have been tested on a large collection of problems with up to 80 jobs.

The National Security Agency (NSA) was authorized in 1983 to construct a semiconductor and circuit-board manufacturing plant at its Ft. Meade, Maryland, facility. This facility was to become known as the Special Process Laboratories (SPL) building. Phase I construction was managed by the US Army Corps of Engineers, Baltimore District (USACE/BD) and commenced in January 1986. Phase I construction provided the basic building and support systems, such as the heating, ventilating, and air-conditioning system, the deionized-water and wastewater-treatment systems, and the high-purity-gas piping system. Phase II construction involved fitting the semiconductor manufacturing side of the building with manufacturing tools and enhancing various aspects of the Phase I construction. Phase II construction was managed by NSA and commenced in April 1989. Argonne National Laboratory (ANL) was contracted by USACE/BD midway through the Phase I construction period to provide quality-assured performance reviews of major facility systems in the SPL. Following completion of the Phase I construction, ANL continued its performance reviews under NSA sponsorship, focusing its attention on the enhancements to the various manufacturing support systems of interest. The purpose of this document is to provide a guide to the files that were generated by ANL during its term of technical assistance to USACE/BD and NSA and to explain the quality assurance program that was implemented when ANL conducted its performance reviews of the SPL building's systems. One set of the ANL project files is located at NSA, Ft. Meade, and two sets are at Argonne, Illinois. The ANL sets will be maintained until the year 2000, or for the 10-year estimated life of the project. 1 fig.

This paper presents a new class of well-behaved Petri nets called resource control net (ERCN*) merged nets that generalize the class of ERCN merged nets proposed in a previous paper by Xie and Jeng. ERCN merged nets can model parallel and synchronized processes in semiconductor manufacturing such as lot split and lot merging, which occurs frequently in a research and development (R&D) semiconductor fab (semiconductor plant) for engineering purposes. However, processing cycles for each resource type must include the initial state of the resource type. In other words, no local processing cycles are allowed. This makes the modeling of degraded behavior in semiconductor manufacturing such as rework, failure, and maintenance difficult. In the current work, this constraint is relaxed under the extended free-choice (EFC) or asymmetric choice (AC) condition. Specifically, for each operation place with degrading outgoing arcs, the FC or AC condition is satisfied. In addition, degraded behavior is modeled as blocks within ERCNs. We show that conditions for liveness and reversibility of an ERCN* merged net correspond to the absence of unmarked siphons. The well-behaved conditions can be transformed into inequalities of the initial marking. Examples are shown to illustrate the proposed methodology.

In this paper, we study the problem of considering the internal behavior of complex machines when solving complex job-shop scheduling problems encountered in semiconductor manufacturing. The scheduling problem in the diffusion area is presented, and the complex structures and behaviors of the different machine types in this area are described. Previous related research is reviewed, and our approach to consider the complexity of these machines with batching constraints when scheduling the diffusion area is described. The main part of the paper presents and discusses numerical experiments on industrial instances to show the benefit of choosing a suitable modeling for complex machines.

A hybrid heuristic algorithm for the 2D variable-sized bin packing problem

Structural modeling of proteins from cryo-electron microscopy (cryo-EM) density maps is one of the challenging issues in structural biology. De novo modeling combined with flexible fitting refinement (FFR) has been widely used to build a structure of new proteins. In de novo prediction, artificial conformations containing local structural errors such as chirality errors, cis peptide bonds, and ring penetrations are frequently generated and cannot be easily removed in the subsequent FFR. Moreover, refinement can be significantly suppressed due to the low mobility of atoms inside the protein. To overcome these problems, we propose an efficient scheme for FFR, in which the local structural errors are fixed first, followed by FFR using an iterative simulated annealing (SA) molecular dynamics protocol with the united atom (UA) model in an implicit solvent model; we call this scheme “SAUA-FFR”. The best model is selected from multiple flexible fitting runs with various biasing force constants to reduce overfitting. We apply our scheme to the decoys obtained from MAINMAST and demonstrate an improvement of the best model of eight selected proteins in terms of the root-mean-square deviation, MolProbity score, and RWplus score compared to the original scheme of MAINMAST. Fixing the local structural errors can enhance the formation of secondary structures, and the UA model enables progressive refinement compared to the all-atom model owing to its high mobility in the implicit solvent. The SAUA-FFR scheme realizes efficient and accurate protein structure modeling from medium-resolution maps with less overfitting.

A successive approach to the solution of the inverse problem of the X-ray diffraction tomography (XRDT) is proposed. It is based on the semi-kinematical solution of the dynamical Takagi–Taupin equations for the σ-polarized diffracted wave amplitude. Theoretically, the case of the Coulomb-type point defect in a single crystal Si(111) under the exact conditions of the symmetric Laue diffraction for a set of the tilted X-ray topography 2D-images (2D projections) is considered provided that the plane-parallel sample is rotated around the diffraction vector [bar{{bf{2}}}20]. The iterative simulated annealing (SA) and quasi-Newton gradient descent (qNGD) algorithm codes are used for a recovery of the 3D displacement-field function of the Coulomb-type point defect. The computer recovery data of the 3D displacement-field function related to the one XRDT 2D projection are presented. It is proved that the semi-kinematical approach to the solution of the dynamical Takagi–Taupin equations is effective for recovering the 3D displacement-field function even for the one XRDT 2D projection.

This paper presents the comparative use of Simulated Annealing (SA) and Genetic Algorithm (GA) in a scheduling problem of unrelated parallel machines with set-up times. The problem accounts for allotting batched work parts to the unrelated parallel machines, where each batch is composed of a fixed number of identical jobs. Processing time of each job of a batch is determined according to the machine group as well as the batch group to which the job belongs. Major or minor set-up times are required between two subsequent batches, depending on the batch sequence but yet independent of machines. The objective of the problem is to minimise the Total Weighted Tardiness (TWT) for the unrelated parallel machine scheduling. SA and GA algorithms are proposed to obtain near-optimal solutions of the problem. The performance of proposed heuristics is compared through computational experiments with real data from dicing operations of a compound semiconductor manufacturing facility.

In this paper, we study the problem of considering the internal behavior of complex machines when solving complex job-shop scheduling problems encountered in semiconductor manufacturing. The scheduling problem in the diffusion area is presented, and the complex structures and behaviors of the different machine types in this area are described. Previous related research is reviewed, and our approach to consider the complexity of these machines with batching constraints when scheduling the diffusion area is described. The main part of the paper presents and discusses numerical experiments on industrial instances to show the benefit of choosing a suitable modeling for complex machines.

Enhancing manual inspection in semiconductor manufacturing with integrated augmented reality solutions

This study addresses the complex 2-stage hybrid flow shop (HFS) scheduling problem, which involves urgent and normal jobs in a multi-agent context. The goal is to simultaneously minimize the makespan for normal jobs and the total tardiness for urgent jobs. Our proposed approaches include a Mixed-Integer Programming (MIP) model, list scheduling algorithms (MEDD, MEDD2), constructive algorithms (MNEH, MN-NEH, MFL, MN-MFL), and a simulated annealing (SA) metaheuristic to find practical solutions. Extensive experiments, using randomly generated instances which reflect real manufacturing environments, validate the algorithms' performance. The SA algorithm consistently exhibits a superior and robust performance across all scenarios, finding solutions remarkably close to the optimal. Additionally, MFL and MN-MFL show noteworthy efficiencies and stable performances, indicating their potential for practical applications in rapid decision-making environments.

Educational timetabling is one of the most researched topics in the field of timetabling. This problem consists of allocating a set of lectures to available rooms and periods, considering students and teachers requests and constraints. Several mathematical models for this problem can be found in the literature. The model considered in this paper is based on courses curricula of a university, proposed in the second International Timetabling Competition (ITC-2007). A maximum flow partial solution is used together with the GRASP constructive algorithm to generate a local solution improved by Simulated Annealing. Computational experiments were performed in ITC-2007 instances, and the results were compared to the best solutions of ITC-2007 and to the literature.

Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction

A construction algorithm is suggested to design guide path networks for automated guided vehicle systems (AGVSs). This study uses the total travel time, including waiting and interference time, of vehicles as the decision criteria for determining the direction of path segments on unidirectional guide path layouts. The Q-learning technique is applied to estimate the travel times of vehicles on path segments. Computational experiments were performed to evaluate the performance of the proposed algorithm. The simulation results showed that the proposed algorithm is superior to Kim & Tanchoco's (1993) algorithm in terms of the average travel time, the interference time and the number of deliveries.