The Chemical-mechanical polishing (CMP) process is now widely employed in the ultralarge scale integration chip fabrication. Due to the continuous advances in semiconductor fabrication technology and decreasing sub-micron feature size, the characterization of erosion, which affects circuit performance and manufacturing throughput, has been an important issue in Cu CMP. In this paper, the erosion in Cu CMP is divided into two levels. The wafer-level and die-level erosion models were developed based on the material removal rates and the geometry of incoming wafers to the Cu CMP process, including the Cu interconnect area fraction, linewidth and Cu deposition thickness. Experiments were conducted to obtain the selectivity values between the Cu, barrier layer and dielectric, and the values of within-wafer material removal rate ratio, β, for the validation of the new erosion model. It was compared with the existing models and was found to agree better with the experimental data. / Singapore-MIT Alliance (SMA)

半導體產業在近二十年來一直在我國經濟發展舞台上扮演積極重要的角色，其績效自然是研究者欲探索之重點。在管理會計頗有作用的作業制成本制度，其提供不同於以往傳統成本制度之資訊，在在替全球知名企業創造更具優勢的管理績效。本研究之重心即在：以深入訪談及田野實證研究方式探討半導體公司導入作業制成本制度後，所發生的績效變化、變化時間與相關資訊，用實證資料檢視作業制成本制度與公司實際經營績效之間的關係，為作業制成本制度在企業之成效作一較為完整且具實務運用的描述，進而提供後續推行作業制成本制度之研究價值，並給予實務界導入作業制成本制度之相關資訊，以作為我國企業推動作業制成本管理制度之參考。 　　本研究以田野實證之資料為主，並以迴歸方式驗證作業制成本資訊釋出前後對企業生產成本與品質之影響，實證結果顯示成本會隨資訊釋出而降低，品質則不會有所變化，表示作業制成本制度確實可以幫助管理者從事成本抑減之工作；也得到品質不會在短期內改善之結論，暗示作業制成本制度之財務績效先於品質績效，資訊使用者之熟練程度差異可能影響績效之出現與否。 　　基於研究所得結論，本研究建議個案公司可以將成本下降之經驗擴散至全廠區，藉由新制度教育員工成本與獲利觀念，並針對員工之使用感想修正個案公司之作業制成本制度。同時也建議未來的研究者，對品質與作業制成本制度甚至成本之關係，作更進一步之研究。 / Using field study method, this study focuses on the relationship between the manufacturing performance of the semi-conductor industry and activity-based costing system information. The analysis suggests that with better decision based on activity and cost driver information, the actvity-based costing information will reduce manufacturing costs while leave quality unchanged. 　　In conclusion, empirical results reveal that manufacturing costs actually decrease with the release of activity-based costing information, while manufacturing quality remains the same. The finding shows that the release of activity-based costing information can reduce the manufacturing costs, but there may not be an immediate effect of quality improvement, suggesting that cost can actually be improved by the activity-based costing system, and quality may need more time and skill toward activity-based costing to change.

This study armed to develop a biofilter packed only with fern chips for the removal of air-borne low concentration VOCs (volatile organic compounds) emitted from semiconductor manufacturing industries. The fern chip biofilters could avoid the shortcomings of traditional media, such as compaction, drying, and breakdown, which lead to the performance failure of the biofilters. Performance of biofiltration for removal of simulated semiconductor manufacturing emitted gases consisting of IPA (isopropyl alcohol), acetone, HMDS (hexamethylene disilazane), PGME (propylene glycol monomethyl ether), and PGMEA (propylene glycol monomethyl ether acetate) was studied in a pilot-scale biofilter consisted of two columns (40-cmW x 40-cmL x 70-cmH acrylic column) arranged in series. Each column was packed with fern chips to a packing volume of around 56 L (0.40 m¡Ñ0.40 m¡Ñ0.35 mH). A sprinkler was set over the packed fern chips for providing them with water and nutrition solutions. Liquid leached from both layers of chips were collected in the bottom container of the column. The experiment lasted for 182 days which was divided into four phases with varying influent gas flow rates and VOC concentrations. Gas samples collected around 3 times per week from the influent as well a the first and second stage effluents were analyzed for VOC concentrations. On a weekly basis, fern chips sampled from each column were also analyzed for getting pH, moisture, and the absorbed VOC content of the chips. Phase shifted if it obtained a quasi-steady state which was judged by the nearly unchanging VOC removal efficiencies. Operation conditions of an empty bed retention time (EBRT) of 1.50 min and influent VOC concentrations of 159-284 mg/m3 were used in the Phase I experiment which lasted for 15 days. Nutrition of 1.34 g milk powder/m3.d was used in this phase and the conditions gave an average volumetric VOC loading (L) of 15.1 g/m3.h. Effluent VOC concentrations were 3-18 mg/m3 and an average VOC removal of 96% was obtained in this phase. An EBRT of 0.75 min, L of 11.44 g/m3.h, and nutrition of 1.34 g milk powder/m3.d were used in the Phase II experiment. VOCs in the gas could be removed from 90-126 to 1-19.6 mg/m3 and an average efficiency of 94% was obtained. Following Phase II, an average VOC removal of only 48% was obtained with an EBRT of 0.75 min, nutrition of 2.0 g milk powder/m3.d, and L of 22.8 g/m3.h in Phases III experiment during the 56-97th days from the startup time. Additional nitrogen (urea) and phosphorus (potassium dihydrogen phosphate) was added to the media from the 105th day and the VOC removal increased to 80% at the 107th day. An average VOC removal of around 93% was obtained in phase III experiment. The results showed that enough nutrition is essential to the successful performance for the biofiltration process. Phase IV experiment lasted for 59 days with an EBRT of 0.75 min, L of 34.1 g/m3.h, and nutrition of 2.0-6.0 g/m3.d. During the initial period of this phase, media pH dropped from 7.8 to 5.8 due to an excess nitrogen (ammonium chloride) addition as high as 12.35 g N/m3.d which resulted in nitrification reaction in the media. By stopping nitrogen, increasing milk powder dosing, and addition of NaHCO3 at the 140th day, pH restored to 7.5 in the following days. VOC removal increased to an average of 92% in the rest operation days. From the results, it could be proposed that for achieving over 90% of the VOC removal, appropriate operation conditions are media moisture content = 52-65%, media pH = 7-8, influent VOC concentration = 150-450 mg/Am3, EBRT = 0.75 min, and L to the whole media = 11-34 g/m3.h.

La maîtrise du rendement d’un site de fabrication et l’identification rapide des causes de perte de qualité restent un défi quotidien pour les industriels, qui font face à une concurrence continue. Dans ce cadre, cette thèse a pour ambition de proposer une démarche d’analyse permettant l’identification rapide de l’origine d’un défaut, à travers l’exploitation d’un maximum des données disponibles grâce aux outils de contrôle qualité, tel que la FDC, la métrologie, les tests paramétriques PT, et le tri électriques EWS. Nous avons proposé une nouvelle méthode hybride de fouille de données, nommée CLARIF, qui combine trois méthodes de fouille de données à savoir, le clustering, les règles d’association et l’induction d’arbres de décision. Cette méthode se base sur la génération non supervisée d’un ensemble de modes de production potentiellement problématiques, qui sont caractérisés par des conditions particulières de production. Elle permet, donc, une analyse qui descend au niveau des paramètres de fonctionnement des équipements. L’originalité de la méthode consiste dans (1) une étape de prétraitement pour l’identification de motifs spatiaux à partir des données de contrôle, (2) la génération non supervisée de modes de production candidats pour expliquer le défaut. Nous optimisons la génération des règles d’association à travers la proposition de l’algorithme ARCI, qui est une adaptation du célèbre algorithme de fouille de règles d’association, APRIORI, afin de permettre d’intégrer les contraintes spécifiques à la problématique de CLARIF, et des indicateurs de qualité de filtrage des règles à identifier, à savoir la confiance, la contribution et la complexité. Finalement, nous avons défini un processus d’Extraction de Connaissances à partir des Données, ECD permettant de guider l’utilisateur dans l’application de CLARIF pour expliquer une perte de qualité locale ou globale. / Controlling the performance of a manufacturing site and the rapid identification of quality loss causes remain a daily challenge for manufacturers, who face continuing competition. In this context, this thesis aims to provide an analytical approach for the rapid identification of defect origins, by exploring data available thanks to different quality control systems, such FDC, metrology, parametric tests PT and the Electrical Wafer Sorting EWS. The proposed method, named CLARIF, combines three complementary data mining techniques namely clustering, association rules and decision trees induction. This method is based on unsupervised generation of a set of potentially problematic production modes, which are characterized by specific manufacturing conditions. Thus, we provide an analysis which descends to the level of equipment operating parameters. The originality of this method consists on (1) a pre-treatment step to identify spatial patterns from quality control data, (2) an unsupervised generation of manufacturing modes candidates to explain the quality loss case. We optimize the generation of association rules through the proposed ARCI algorithm, which is an adaptation of the famous association rules mining algorithm, APRIORI to integrate the constraints specific to our issue and filtering quality indicators, namely confidence, contribution and complexity, in order to identify the most interesting rules. Finally, we defined a Knowledge Discovery from Databases process, enabling to guide the user in applying CLARIF to explain both local and global quality loss problems.