various representation to different customers. Transparent customization means that manufacturers provide unique products without needing to inform customers. Silveira et al. [54] surveyed the earlier studies on MC to point out the visionary and practical conceptualizations of MC theory; also, fundamental requirements for developing a basic MC framework composed from eight generic levels of MC were thoroughly discussed in [54]. Further, as information technologies evolves, Fogliatto et al. [55] updated the latest successful MC applications among various fields, including the food industry, electronics, large engineered products, mobile phones, and personalized nutrition; or special MC applications such as homebuilding and the production of foot orthoses. They clearly identified required conditions in different fields and situations of implementing a suitable MC platform from the view of economics, success factors, enablers, and customer‐manufacturer interactions.
For manufacturers, two mandatory factors of agility and quick responsiveness to manufacturing changes are expected to minimize the escalating costs [51, 53, 55]. They have to ensure the production facility must be flexible enough for switching between complex variants with some delay and be agile enough to adapt to changes in customized products at a low cost, thereby retaining economic benefits [55, 56]. For customers, after the emergence of Industry 4.0, the state‐of‐the‐art of IoT/CPS replaces traditional MC scenarios, and gives customers more chances to actively participate in a collaborative design of customized products.
However, no matter how production technologies are improved in the era of Industry 4.0, the ultimate aim for manufacturing has not changed, which is the manufacturing quality of products. Manufacturers are imperative to ensure that the manufacturing quality of deliverables conforms to the design specifications before delivering them to customers. Thus, “quality control” is also listed as one of the promising areas to be achieved for future research in MC [55]. Namely, how to effectively minimize the defective product cost is still the biggest challenge of MC. As such, a fully automated and real‐time total‐inspection method is needed to withstand a global requirement on increasing product quality and reducing production cost.
1.2.3 Zero Defects – Vision of Industry 4.1
Since the late 1960s, ZD has been one of the quality‐improvement objectives for accomplishing manufacturing quality [57]. Through prevention methods, ZD aims to boost production and minimize waste. ZD is based on the concept that the amount of mistakes a worker makes doesn't matter since inspectors will catch them before they reach customers [57].
Industry 4.0, since its first presentation at the Hannover Messe 2014, is set to be one of the new manufacturing objectives and, most of all, keep the faith of achieving nearly ZD state in the manufacturing industry [58, 59]. The current Industry 4.0 related technologies emphasize on productivity improvement but not on quality enhancement; in other words, they can only keep the faith of achieving nearly ZD state without realizing this goal. The key reason for this inability is the lack of an affordable online and real‐time Total Inspection technology. By adopting the Automatic Virtual Metrology (AVM) technology that has been certified with the invention patents from six countries (Taiwan ROC, USA, Japan, Germany, China, and Korea) developed by the research team of Fan‐Tien Cheng, the Editor and main author of this book, ZD can be achieved as AVM can provide the Total Inspection data of all products online and in real time. A defective product will be discarded once it is detected by AVM; in this way, all of the deliverables will be ZD. Further, the Key‐variable Search Algorithm (KSA) of the Intelligent Yield Management (IYM) system developed by Fan‐Tien Cheng’s research team can be utilized to find out the root causes of the defects for continuous improvement on those defective products. As such, ZD of all products can be achieved. Therefore, once AVM and IYM are integrated into the successfully developed Industry 4.0 platform, the state of ZD can be realized, which is defined as Industry 4.1 by Fan‐Tien Cheng. The concepts of Industry 4.1 were disclosed in IEEE Robotics and Automation Letters in January 2016 [60]. The technical details of AVM and IYM will be elaborated in Chapters 8 and 10, respectively.
1.2.3.1 Two Stages of Achieving Zero Defects
Generally speaking, two stages are involved for achieving Zero Defects in Industry 4.1:
Stage I: accomplish Zero Defects of all the deliverables by applying efficient and economical total‐quality‐inspection techniques.
Stage II: ensure Zero Defects of all the products gradually by improving the yield with big data analytics and continuous improvement.
Stage I can be accomplished by directly applying AVM to perform Total Inspection on all the possible deliverables. If any defects are found in a possible deliverable, then this one should not be delivered to customers. As a result, the goal of ZD for all the deliverables is achieved.
The manufacturing‐related data of all the defective products found in Stage I should be collected such that the KSA in IYM can be performed on those data to find out the root causes that result in the defects. Then, those root causes should be fixed for reducing possible defects that may occur in the subsequent production run. As such, the goal of nearly ZD for all the products can be accomplished by continuous improvements. The process mentioned in this paragraph is the so‐called Stage II.
1.3 Development Strategy of Intelligent Manufacturing with Zero Defects
As semiconductor manufacturing technologies advance, semiconductor manufacturing processes are becoming more and more sophisticated. Thus, how to maintain their feasible production yield becomes an important issue. As shown in Figure 1.12, during the product life cycle, the product yield (blue solid line) gradually rises up in the research‐and‐development (RD) phase and ramp‐up phase and then keeps steady in the mass‐production (MP) phase. On the contrary, the product cost (red solid line) continuously decreases during the production life cycle. If a company can improve its changing curves of yield and cost from the solid lines into their corresponding segmented lines, the company’s competitiveness would be enhanced effectively. This implies that rapidly increasing the yield in the RD phase to transfer products into the MP phase, and then assuring the yield in the MP phase while promptly finding out and resolving the root causes of yield losses is a feasible strategy for increasing the company’s competitiveness. However, no literature has proposed a systematic approach of enhancing and assuring production yield, which targets both the RD phase and the MP phase of the product life cycle.
Figure 1.12 Changing curves of yield and cost during the product life cycle.
Source: Reprinted with permission from Ref. [61]; © 2017 IEEE.
In the following, a five‐stage approach as shown in Figure 1.13 for enhancing production yield and assuring nearly ZD, taking a semiconductor bumping process as an illustrative example, is proposed.
Figure 1.13 Five‐stage strategy for increasing yield in RD/ramp‐up and MP phases of a manufacturing process.
1.3.1 Five‐Stage Strategy of Yield Enhancement and Zero‐Defects Assurance