A Wafer Bin Map "Relaxed" Clustering Algorithm for Improving Semiconductor Production Yield

AbstractThe semiconductor manufacturing process involves long and complex activities, with intensive use of resources. Producers compete through the introduction of new technologies for increasing yield and reducing costs. So, yield improvement is becoming increasingly important since advanced production technologies are complex and interrelated. In particular, Wafer Bin Maps (WBMs) presenting specific fault models provide crucial information to keep track of process problems in semiconductor manufacturing. Production control is often based on the "judgement" of expert engineers who, however, carry out the analysis of map templates through simple visual exploration. In this way, existing studies are subjective, time consuming, and are also limited by the capacity of human recognition. This study proposes a network-based data mining approach, which integrates correlation graphs with clustering analysis to quickly extract patterns from WBMs and then bind them to manufacturing defects. An empirical study has been conducted on real production data for validating the proposed clustering algorithm, which showed a perfect correspondence between the malfunction patterns found by the algorithm and those discovered by human experts, so confirming the validity of our approach in its ability of correctly identifying actual defective patterns to help improving production yield

Cost-Effective TSV Grouping for Yield Improvement of 3D-ICs

Three-dimensional Integrated Circuits (3D-ICs) vertically stack multiple silicon dies to reduce overall wire length, power consumption, and allow integration of heterogeneous technologies. Through-silicon-vias (TSVs) which act as vertical links between layers pose challenges for 3D integration design. TSV defects can happen in fabrication process and bonding stage, which can reduce the yield and increase the cost. Recent work proposed the employment of redundant TSVs to improve the yield of 3D-ICs. This paper presents a redundant TSVs grouping technique, which partition regular and redundant TSVs into groups. For each group, a set of multiplexers are used to select good signal paths away from defective TSVs. We investigate the impact of grouping ratio (regular-to-redundant TSVs in one group) on trade-off between yield and hardware overhead. We also show probabilistic models for yield analysis under the influence of independent and clustering defect distributions. Simulation results show that for a given number of TSVs and TSV failure rate, careful selection of grouping ratios lead to achieving 100% yield at minimal hardware cost (number of multiplexers and redundant TSVs) in comparison to a design that does not exploit TSV grouping ratios

A regional model of endogenous growth with creative destruction

We consider a two region growth model with vertical innovations where technical externalities in R&D lead to a technology leading region being the most attractive location for innovative firms. Innovations are produced in the form of quality improvements building on available knowledge and firms choose a technologically advanced location to maximise the productivity of R&D and maintain their niche monopoly. The partial nature of spillovers causes an additional force for agglomeration: the clustering effect. Agglomerated locations have the benefit of local inter-varietal knowledge spillovers for growth while peripheral locations depend on trade and regional knowledge spillovers

Knowledge discovery for friction stir welding via data driven approaches: Part 2 – multiobjective modelling using fuzzy rule based systems

In this final part of this extensive study, a new systematic data-driven fuzzy modelling approach has been developed, taking into account both the modelling accuracy and its interpretability (transparency) as attributes. For the first time, a data-driven modelling framework has been proposed designed and implemented in order to model the intricate FSW behaviours relating to AA5083 aluminium alloy, consisting of the grain size, mechanical properties, as well as internal process properties. As a result, ‘Pareto-optimal’ predictive models have been successfully elicited which, through validations on real data for the aluminium alloy AA5083, have been shown to be accurate, transparent and generic despite the conservative number of data points used for model training and testing. Compared with analytically based methods, the proposed data-driven modelling approach provides a more effective way to construct prediction models for FSW when there is an apparent lack of fundamental process knowledge

Modeling and Optimal Design of Machining-Induced Residual Stresses in Aluminium Alloys Using a Fast Hierarchical Multiobjective Optimization Algorithm

The residual stresses induced during shaping and machining play an important role in determining the integrity and durability of metal components. An important issue of producing safety critical components is to find the machining parameters that create compressive surface stresses or minimise tensile surface stresses. In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows constructing transparent fuzzy models considering both accuracy and interpretability attributes of fuzzy systems. The new method employs a hierarchical optimisation structure to improve the modelling efficiency, where two learning mechanisms cooperate together: NSGA-II is used to improve the model’s structure while the gradient descent method is used to optimise the numerical parameters. This hybrid approach is then successfully applied to the problem that concerns the prediction of machining induced residual stresses in aerospace aluminium alloys. Based on the developed reliable prediction models, NSGA-II is further applied to the multi-objective optimal design of aluminium alloys in a ‘reverse-engineering’ fashion. It is revealed that the optimal machining regimes to minimise the residual stress and the machining cost simultaneously can be successfully located

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA