1.6
Conclusion et Perspectives
Dans ma th`ese, je me suis int´eress´e au probl`eme de la mise œuvre industrielle des plans de contrˆole dynamique. Apr`es avoir analys´e et mis en ´evidence la com- plexit´e de la conception des plans de contrˆole dans une industrie multi-produits, j’ai d´evelopp´e et propos´e de nouvelles solutions que j’ai fait valid´e au travers des prototypes, simulations, et int´eractions avec diff´erents experts. Toutes les solutions propos´ees ont ´et´e valid´ees industriellement et certaines ont ´et´e industrialis´ees au sein du site 300mm de STMicroelectronics `a Crolles, en France. Les diff´erentes solutions ont ´et´e comminqu´ees et publi´ees dans des congr`es, conf´erences et journaux interna- tionaux. Une des communications a ´et´e recompens´ee avec le prix de Best Student Paper Award [60].
Plusieurs pistes ont ´et´e explor´ees ouvrant la voie `a diverses perspectives. Les deux principales perspectives concernent l’optimisation de la gestion des ex- cursions et l’´echantillonnage pr´edictif. Concernant la gestion des excursions, le champ d’analyse (investigation ou recherche de la source du probl`eme) pourrait ˆetre r´eduit en utilisant la notion d’ensemble dominant o`u le lot prioritaire `a inspecter serait celui qui apporte le maximum d’informations sur l’ensemble des lots poten- tiellement impact´es. En ce qui concerne l’´echantillonnage pr´edictif, l’id´ee serait de ne plus s´electionner les lots en consid´erant uniquement les lots devant une ´etape de contrˆole mais d’int´egrer aussi des “lots futurs” c’est-`a-dire des lots suppos´es arriver devant l’´etape de contˆole dans un “futur” tr`es proche.
General Introduction
Semiconductor manufacturing is made of numerous and repetitive processing steps resulting in cycle times of more than two months. With the reduction in de- vice sizes, re-entrant flows (repetition of similar processing steps), and the variety of products to be manufactured (more than 200 products in high-mix plants), the com- plexity has strongly increased in recent years. This complexity brings semiconductor manufacturers to introduce several layers of controls in order to guarantee high yield within production. However, most control operations are considered as non-added value and thus, when a control operation is introduced, cycle times increase with consequences on the final product costs. In the context of worldwide competition, companies have to provide pricing power against competitors. This implies that companies have to be able to sustain high yield with a minimum number of control operations.
Several works have been conducted on sampling techniques with the aim of min- imizing the number of control operations without increasing the risk (i.e. material at risk) in production. Compared to static techniques, dynamic sampling tech- niques are more suitable for modern and high-mix semiconductor plants because they integrate factory dynamics and variability. However, the problem is in the industrial implementation of dynamic sampling approaches. The specificity of each semiconductor plant, the IT infrastructure, the variability of production flows, the heterogeneity of information systems, and the customer requirements are factors that strongly increase the complexity, leading to impracticability of many sampling algorithms proposed in the literature. The required investments are such that com- panies prefer to keep static sampling strategies whereas their inability to quickly
detect process drifts has already been pointed out.
This thesis aims at analyzing the efficiency of sampling policies, identifying breaches of controls, i.e. places throughout the process flow where control oper- ations might be introduced or removed, assessing the added-value of each control operation, understanding why dynamic sampling techniques are seen efficient but most of the time impracticable, and providing novel solutions and approaches that can be industrialized. The thesis is realized within the framework of the Conven- tions Industrielles de Formation par la REcherche (CIFRE), in accordance with the Association Nationale de la Recherche Technique (ANRT) which supports com- panies that hire PhD students. The thesis is also written as a part of the Euro- pean Union project IMPROVE (Implementing Manufacturing science solutions to increase equiPment pROductiVity and fab pErformance).
Reading plan
Generally, a scientific work is done according to the following schema [14]: 1. Problem definition,
2. State of the art review (literature review), 3. Case study,
4. Solution proposal, 5. Tests and validation,
6. Generalization and perspectives.
However, this is a thesis in an industrial context through a joint collaboration between industry and academics. There is an industrial problem and a research cen- ter must define the problem and propose innovative solutions. The case study comes before the literature review and proposed solutions are based on existing systems. Our work is thus structured into 7 main chapters:
GENERAL INTRODUCTION
- Chapter 1: Industrial Context.
- Chapter 2: Problem Identification and Research Issues. - Chapter 3: Literature Review on Sampling Techniques. - Chapter 4: Analyzing and Optimizing Control Plans. - Chapter 5: Implementing Smart Sampling Policies. - Chapter 6: Optimizing Smart Sampling Policies.
- Chapter 7: Industrial Developments and Implementations.
This decomposition can be linked to the TRIZ13approach [4] developed in 1946
by Genrich S. Altshuller for solving technical problems. The TRIZ approach is characterized by four main steps (Figure 1.10):
1. Problem identification and formulation. 2. Concept generation and comparison. 3. General solution.
4. Specific solution embodiment.
Figure 1.10: General problem solving model (TRIZ approach) [90].
Using the TRIZ approach, we can classify our work into these four main steps: - Chapters 1 and 2 refer to problem identification and formulation.
- Chapter 3 refers to concept generation and comparison.
13Teoriya Resheniya Izobretatelskikh Zadatch. In English, it is defined as Theory of Inventive
- Chapter 4, 5, and 6 refer to general solutions.
- Chapter 7 refers to specific solution and embodiment.
Figure 1.11: Thesis reading plan.
Chapter 1 introduces the industrial context. A description of the semiconduc- tor industry is given, the main manufacturing steps are introduced, and controls performed throughout the production are presented.
Chapter 2 describes the problem tackled in this thesis. The specificities of STMi- croelectronics Crolles are presented, and the thesis questions are introduced.
Chapter 3 surveys the literature on sampling techniques for controls in semicon- ductor manufacturing. Each sampling technique is reviewed through statements, critical analyses, and discussions on industrial deployments.
GENERAL INTRODUCTION
Chapter 4 analyses the impact of variability on static control plans, and intro- duces the fab-wide indicator (IPC) that has been developed to support the industrial implementation of dynamic control plans.
Chapter 5 introduces the dynamic sampling algorithms that have been developed within the framework of the European project IMPROVE.
Chapter 6 is devoted to optimizing solutions presented in chapter 4 and chapter 5.
Chapter 7 presents some prototypes that have been developed and deployed within the company during the thesis. These prototypes have been used to vali- date the novel approaches and algorithms that have been industrialized throughout the thesis.
The last part of the document is dedicated to a general conclusion and perspec- tives for further research.
Chapter 2
Industrial Context
This chapter introduces the context of the thesis: Semiconductor manufacturing and controls during the production. The focus is put on controls and especially on in-line measurements that aim at monitoring process and tool variations. The de- scription of the different types of controls shows an important complexity linked to the size of manufactured products (Integrated Circuits). This thesis mainly addresses Defectivity controls where the objective is to detect and reduce particles generated on wafers during the production. All production tools are concerned and the variability within the production environment is such that the efficiency of a Defectivity control plan is never guaranteed. Hence our interest for this challenging problem.
2.1 Introduction