Combining negative binomial and Weibull distributions for yield and reliability prediction

A key productivity metric in semiconductor manufacturing is wafer test yield - the fraction of dies deemed functional following wafer probe testing. Wafer test yield is directly related to semiconductor manufacturing profitability: The higher the yield, the lower the cost of producing a functional chip, and therefore the greater the potential profit. Because wafer test yield is such a critical variable in a products profit potential, accurate yield projection models are essential to semiconductor manufacturers economic success. It is important to understand the correlation between defects causing yield loss and defects causing reliability failures. This article presents a modeling methodology and supporting data, demonstrating that yield and reliability defects can be directly linked in a unified model.     

Printed circuit pack (PCP) yield prediction

Yield prediction is important to enable prediction of full scale production, based on product design, manufacturing processes, and production methods. A model for yield prediction and associated cost for printed circuit pack (PCP) is given. An application for a PCP and conclusions are presented.     

Multiple platforms design and product family process planning for combined additive and subtractive manufacturing

Industry 4.0 promotes the utilization of new exponential technologies such as additive manufacturing in responding to different manufacturing challenges. Among these, the integration of additive and subtractive manufacturing technologies can play an important role and be a game changer in manufacturing products. In addition, using product platforms improves the efficiency and responsiveness of manufacturing systems and is considered an enabler of mass customization. In this paper, a model to design multiple platforms that can be customized using additive and subtractive manufacturing to manufacture a product family cost-effectively is proposed. The developed model is used to determine the optimal number of product platforms, each platform design (i.e. its features set), the assignment of each platform to various product variants, and the macro process plans for customizing the platforms while minimizing the overall product family manufacturing cost.The multiple additive/subtractive platforms and their process plans are determined by considering not only the commonality between the product variants but also their various manufacturing cost elements and the customer demand of each variant. The design of multiple product family platforms and their process plans is NP-hard problem. A genetic algorithm-based model is developed to reduce the computational complexity and find optimal or near optimal solution. Two case studies are used to illustrate the developed multiple platform model. The model results were compared with a single platform model in literature and the results demonstrate the multiple platform model superiority in manufacturing product families in lower cost. The use of the developed model enables manufacturing product families cost efficiently and allows manufacturers to manage diversity in products and market demands.     

Tolerance analysis based on Monte Carlo simulation: a case of an automotive water pump design optimization

Successful products are those presenting the highest quality at a fair cost. Although different approaches can be used to define the concept of quality, functional reliability is always a major requirement, due to implications such as safety and user losses regarding maintenance expenses, and product availability. Intelligent designs are robust and result in a fair cost. Robust designs are those insensitive to sources of variation occurring during the product life, keeping their performance under variable use conditions, like thermal and stress effects. The robustness approach is a function of two main design criteria: low complexity and tolerance design. Design for manufacture and assembly is closely related to decreasing complexity. Tolerance design is a tool in which the unavoidable manufacturing variations are considered during product development. This work presents a proposal for an intelligent design in an actual application by considering design simplification through the reduction of parts for an automotive water pump. The tolerance analysis is performed by means of a powerful statistical approach—a Monte Carlo simulation—in which process behavior is randomly simulated representing a high production volume. Additionally, service thermal effects are also contemplated, and assembly tests are proposed for automatic rejection of non-conforming parts, assuring high reliability and full compliance with functional requirements. This is an example of integrated design–manufacturing work aiming at both cost saving and improved reliability.

     

Fuzzy logic-based forecasting model

In this paper a fuzzy logic-based software tool, fuzzy logic advisory tool (FLAT), for demand forecasting of signal transmission products is presented. The FLAT was developed for the prediction of demand of about 1000 different products in order to aid materials purchasing process of about 14,000 different components in the electronics manufacturing processes of Nokia Network Systems's Haukipudas factory. The prediction values of different products are inferred by starting from a set of eight input values. Each input value is fuzzied by the FLAT. Thereafter, fuzzy results are inferred in three sequential phases. In each phase the number of variables is split due to hierarchical structure of the inference module. A data base and a rule base are divided accordingly into three hierarchical levels. Rules are represented by linguistic relations changed into matrix equations form in order to apply linguistic equations framework technique (LE). Fuzzy membership functions for input values are determined on-line from earlier input values of the products. Fuzzy rules were inferred by analyzing behavior of the products together with market experts and product experts of the company. The model is able to produce more accurate decision-making support than more traditional approaches. This is probably due to the model-based approach and systematic data management.     

An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, job-cluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.     

Analyzing manufacturing test costs

We can think of the rapidly growing electronics manufacturing services (EMS) industry as an electronics manufacturing supermarket. Like their food store counterparts, EMS providers are usually geographically convenient to their major customers, original equipment manufacturers (OEMs). They provide a smorgasbord of goods and services ranging from circuit design through final product assembly and shipping of printed circuit boards-all on a fast-turnaround basis. And, like supermarkets, EMS companies are highly competitive and differentiate themselves primarily on intangibles such as quality of service, turnaround time, and the like. The economic model of the EMS provider is also strikingly similar to the supermarket's: profitability depends on high volumes shipped at relatively low gross margins. Historically, EMS profit has hovered in the neighborhood of 5% to 10%. Operating this close to zero or even negative profitability allows an EMS sales department little room for error in pricing and quoting jobs for customers. Most of the EMS job-pricing formula depends on tangible costs such as parts, labor, and overhead consisting of the amortized operating cost of the surface mount technology (SMT) assembly line. In circuit-board manufacturing, however, ill-defined quality criteria and an imprecise understanding of test costs often result in a gap between the job cost estimate and the actual production cost     

A path planning algorithm for PCB surface quality automatic inspection

Journal of Intelligent Manufacturing - The surface quality inspection of industrial printed circuit board (PCB) is a vitally important link in its manufacturing process. To inspect surface defects...     

Developing a high-level fault simulation standard

Recent developments in deep-submicron technology challenge current integrated circuit testing methods. The increasing complexity of designed systems makes test development more time-consuming. Moreover, nanometer technology introduces new defects or higher data rate errors. To reduce manufacturing costs and time to market, we must develop efficient fault detection and location methods. Using high-level fault simulation stimulates the development of new, fast test-generation algorithms that take into consideration functional features of the system under test or its components. Moreover, all synthesis tools migrate to higher levels, and we believe that this will improve ATPG tools as well     

Product architecting for personalization

Personalization is an emerging manufacturing paradigm whereby customers can tailor products to their individual needs while maintaining high production efficiency. This paradigm necessitates “personalized product architecting” for determination of customizable/personalizable product modules and cost-effective manufacturing methods. This paper presents an initial effort in developing a method for identifying appropriate product architectures and manufacturing resolutions to achieve personalization considering functional utility and manufacturing cost. Ergonomic experiments and conjoint analysis are implemented to build functions relating manufacturability, price, and utility. Using these functions, a case study based on shoe products is conducted and the common integer programming welfare problem is expanded to a mixed-integer programming optimization problem for determination of a product family incorporating both personalized and customized offerings.     

An approach to monitoring quality in manufacturing using supervised machine learning on product state data

Increasing market demand towards higher product and process quality and efficiency forces companies to think of new and innovative ways to optimize their production. In the area of high-tech manufacturing products, even slight variations of the product state during production can lead to costly and time-consuming rework or even scrapage. Describing an individual product’s state along the entire manufacturing programme, including all relevant information involved for utilization, e.g., in-process adjustments of process parameters, can be one way to meet the quality requirements and stay competitive. Ideally, the gathered information can be directly analyzed and in case of an identified critical trend or event, adequate action, such as an alarm, can be triggered. Traditional methods based on modelling of cause-effect relations reaches its limits due to the fast increasing complexity and high-dimensionality of modern manufacturing programmes. There is a need for new approaches that are able to cope with this complexity and high-dimensionality which, at the same time, are able to generate applicable results with reasonable effort. Within this paper, the possibility to generate such a system by applying a combination of Cluster Analysis and Supervised Machine Learning on product state data along the manufacturing programme will be presented. After elaborating on the different key aspects of the approach, the applicability on the identified problem in industrial environment will be discussed briefly.     

3D-SRAM中的TSV开路故障模型研究

基于3D-IC技术的3D SRAM,由于硅通孔TSV制造工艺尚未成熟,使得TSV容易出现开路故障。而现有的TSV测试方式均需要通过特定的电路来实现,增加了额外的面积开销。通过对2D Memory BIST的研究,针对3D SRAM中的TSV全开路故障进行建模,根据TSV之间的耦合效应进行广泛的模拟研究,分析并验证在读写操作下由于TSV的开路故障对SRAM存储单元里所存值的影响,将TSV开路故障所引起的物理故障映射为SRAM的功能故障。该故障模型可以在不增加额外测试电路的情况下,为有效测试和解决这种TSV开路故障提供基础。     

A model‐driven decision support system for product risk analysis

Abstract: The paper describes a model‐driven decision support system for risk analysis in product development, based on an European Commission‐funded project. The model can be used to simulate the life cycle of a large batch of products and track products in operation and service phases with consideration of design and manufacturing phases. This provides some useful benefits – in particular, being able to generate composite results when the product life is subject to unpredictable events, which makes the system's behaviour non‐linear. A case study is conducted through a domestic appliance in the field trial from a manufacturing company, and the results indicate that the model can enable developers or manufacturers to understand the behaviour and characteristics of products or systems in the future use and to predict the cost of any changes in the pattern of their life.     

Testing Defects in Scan Chains

Applying scan-based DFT, I_DDQ testing, or both to sequential circuits does not ensure bridging-fault detection, which depends on the resistance of the fault and circuit level parameters. With a “transparent” scan chain, however, the tester can use both methods to detect manufacturing process defects effectively-including difficult-to-detect shorts in the scan chain. The author presents a strategy for making the scan chain transparent. The test complexity of such a chain is very small, regardless of the number of flip-flops it contains     

Optimal repair decisions for integrated circuits manufacturing

The sequential manufacturing process has been widely applied to the making of complicated integrated circuits (IC). The basic idea is to break an IC product line into a linear sequence of simple operations, and then, a final product with desired high complexity can be obtained by performing the individual operations step by step. To assure the quality of the in-process products, we may conduct a test after each operation. When an in-process product is fault-free, it will pass the test and be moved to the next operation. On the other hand, for a defective in-process product which fails the test, we have to make a decision to either scrap or repair it so that no fault propagates to later operations. The repair/scrapping decisions certainly affect the manufacturing cost. Our goal is to find a set of optimal repair decisions so that the total manufacturing cost per fault-free IC product is minimized. For an IC product line with n sequential operations, there are 2ⁿpossible decision combinations in total. In this paper, we introduce a dynamic programming approach with an algorithm of 0(n₂) complexity to solve the problem.     

Low‐Cost versus Innovation: Contrasting Outsourcing and Integration Strategies in Manufacturing

This paper analyses how two different outsourcing manufacturing strategies relate to plant performance and innovation capability when taking into account the organizational integration of design and manufacturing as well as product complexity. The study discriminates between low‐cost‐oriented outsourcing and innovation‐oriented outsourcing. The empirical data used is based on a survey of 267 engineering firms, of which half have outsourced manufacturing. We found that the two outsourcing strategies do have different effects, which illustrates that outsourcing represents a trade‐off between improving innovation capability and lowering costs. The study furthermore shows that manufacturing and supplier integration in product design processes is mainly beneficial when applying innovation‐oriented outsourcing, and in particular when products and manufacturing processes are complex.     

The impact of computer-aided manufacturing on total productivity

Labor productivity and the rate of return on investment techniques have been used to justify the application of computer-aided manufacturing. These techniques are partial measures and do not consider the total cost of doing business. This paper presents a step-by-step procedure for assessing the impact of computer-aided manufacturing on total productivity. Two case studies are presented. The first case study deals with the comparison of the total and partial productivities of the manual and computer-aided method of manufacturing printed circuit boards. The second case shows a comparison of the productivities of two types of computer-aided method of assembling printed circuit boards. Both case studies were conducted for a period of ten weeks. Computer system design problems encountered during implementation are discussed as well as ways to correct these problems.     

A BIST and boundary-scan economics framework

IC level built-in self-test and IEEE 1149.1 boundary-scan architecture offer potential benefits at all phases of a product's life cycle: development, manufacturing, and field deployment. During early model debugging, for example, boundary scan rapidly flushes out structural defects such as solder bridges or opens. During manufacturing test, BIST and boundary scan can improve coverage, reduce test and diagnosis time, and reduce test capital. In the field, embedded boundary-scan and BIST capabilities may facilitate accurate system diagnostics that isolate defects to individual field-replaceable units. Before investing in these design-for-testability features, however, a product development team should carefully consider their costs as well as their benefits. So far, tools for accurately evaluating these economic trade offs have not been available. At Lucent Technologies, therefore, we have developed a framework to guide a cost-benefit analysis of an investment in BIST and/or boundary scan. The framework is in its formative stages and will continue to evolve. BIST and boundary scan affect cost at all levels of product integration and during all phases of the product life cycle. This analysis framework helps developers decide if the benefits are worth the costs     

Multi-objective optimization of product variety and manufacturing complexity in mixed-model assembly systems

Product variety has increased dramatically as manufacturers compete for market shares. While higher variety of products may satisfy a broader range of customers, it also introduces complexity in manufacturing. A multi-objective optimization approach is proposed to balance product variety and manufacturing complexity when designing a product family and the mixed-model assembly system. Relative complexity is introduced to measure the complexity and to find the best set of product variants to be offered while balancing market share and complexity. Numerical examples are provided to demonstrate the approach.     

Identifying variety-induced complexity cost factors in manufacturing companies and their impact on product profitability

Many manufacturing companies experience increasing product variety, which not only leads to significantly increased operations’ costs but also to an uneven distribution of these costs across product variants. To deal with this challenge, managers need to accurately quantify the costs of product variety-induced complexity to understand its impact on company revenues. However, quantifying complexity costs is no trivial task, as both the variety-related factors that generate more complexity and the costs implied by these factors are specific to each company. Therefore, we extensively reviewed the relevant literature to build a list of possible complexity cost factors generated by product variety, which are here named variety-induced complexity cost factors (VCCFs). This list is intended to be used by managers as a reference set of VCCFs to help identify and subsequently quantify the costs of product variety-related complexity in manufacturing companies and finally attribute them to end product variants. To evaluate the usefulness of this list, the identified VCCFs were examined in six companies using both the judgment of managers and data from their enterprise resource planning systems. This empirical examination not only provides evidence of the usefulness of this list but also data, rarely available in academic literature, on the costs of product variety-induced complexity and its proportion of company revenue. It also suggests that it is important to research effective ways to get, elaborate, and present data to calculate the costs of product variety-related complexity and reports a viable approach used in six companies.