Neste trabalho, é apresentada uma metodologia para fabricação de transistores de filmes finos orgânicos sobre substratos flexíveis e resultados de simples testes de flexão desses substratos. Foram fabricados transistores de Poli(3-hexiltiofeno) (P3HT) com diversas arquiteturas não se preocupando somente com a relação W/L dessas, mas também com a facilidade de caracterizar os dispositivos em superfícies curvas. Os transistores foram fabricados sobre diversos substratos como silício, vidro e PET, para que fosse possível uma comparação de eficiência entre eles. A mobilidade do P3HT se manteve próximo de 10-2 cm2/Vs enquanto que a corrente de ION apresentou um aumento significativo, Os transistores sobre PET se mostraram resistentes à flexão, suportando raios de curvaturas de até 0,8 cm sem afetar sua resposta. Porém foi identificado que a compressão ou a tração resultam em efeitos diferentes nos transistores, principalmente sobre os eletrodos de ouro. / Presented herein is a fabrication procedure for organic thin film transistors over flexible substrates. It is also shown the results of the bending tests on these devices. Transistors of poly(3-hexyl thiophene) (P3HT), with different architectures were fabricated, aiming not only the W/L relation but also, the capability contacting bent electrodes for testes on curved surfaces. The transistors were fabricated over rigid and flexible substrates like silicon, glass and PET, allowing the efficiency comparison between them. The P3HT mobility was kept stable, around 10-2 cm2/Vs, while the ION current presented a significant change on different substrates. The transistors over PET showed to be resistance to bending, being able to bend to a curve radius of 0.8 cm without losing its transistor characteristics. Although, it was identified that the direction of bending, compression and traction, result on different effects over the transistors, especially over the golden electrodes.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Bibliography: leaves 214-219. / by David Robert Wilson. / Ph.D.

The lithography process for chip manufacturing has been playing a critical role in keeping Moor's law alive. Even though the wavelength used for the process is bigger than actual device feature size, which makes it difficult to transfer layout patterns from the mask to wafer, lithographers have developed a various technique such as Resolution Enhancement Techniques (RETs), Multi-patterning, and Optical Proximity Correction (OPC) to overcome the sub-wavelength lithography gap. However, as feature size in chip design scales down further to a point where manufacturing constraints must be applied to early design phase before generating physical design layout. Design for Manufacturing (DFM) is not optional anymore these days. In terms of the lithography process, circuit designer should consider making their design as litho-friendly as possible. Lithography hotspot is a place where it is susceptible to have fatal pinching (open circuit) or bridging (short circuit) error due to poor printability of certain patterns in a design layout. To avoid undesirable patterns in layout, it is mandatory to find hotspots in early design stage. One way to find hotspots is to run lithography simulation on a layout. However, lithography simulation is too computationally expensive for full-chip design. Therefore, there have been suggestions such as pattern matching and machine learning (ML) technique for an alternative and practical hotspot detection method. Pattern matching is fast and accurate. Large hotspot pattern library is utilized to find hotspots. Its drawback is that it can not detect hotspots that are unseen before. On contrast, ML is effective to find previously unseen hotspots, but it may produce false positives. This research presents a novel geometric pattern matching methodology using edge driven dissected rectangles and litho award machine learning for hotspot detection. 1. Edge Driven Dissected Rectangles (EDDR) based pattern matching EDDR pattern matching employs member concept inside a pattern bounding box. Unlike the previous pattern matching, the idea proposed in this thesis uses simple Design Rule Check (DRC) operations to create member rectangles for pattern matching. Our approach shows significant speedup against a state-of-art commercial pattern matching tool as well as other methods. Due to its simple DRC edge operation rules, it is flexible for fuzzy pattern match and partial pattern match, which enable us to check previously unseen hotspots as well as the exact pattern match. 2. Litho-aware Machine Learning A new methodology for machine learning (ML)-based hotspot detection harnesses lithography information to build SVM (Support Vector Machine) during its learning process. Unlike the previous research that uses only geometric information or requires a post-OPC (Optical Proximity Correction) mask, our method utilizes detailed optical information but bypasses post-OPC mask by sampling latent image intensity and use those points to train an SVM model. Our lithography-aware machine learning guides learning process using actual lithography information combined with lithography domain knowledge. While the previous works for SVM modeling to identify hotspots have used only geometric related information, which is not directly relevant to the lithographic process, our SVM model was trained with lithographic information which has a direct impact on causing pinching or bridging hotspots. Furthermore, rather than creating a monolithic SVM trying to cover all hotspot patterns, we utilized lithography domain knowledge and separated hotspot types such as HB(Horizontal Bridging), VB (Vertical Bridging), HP(Horizontal Pinching), and VP(Vertical Pinching) for our SVM model. Out results demonstrated high accuracy and low false alarm, and faster runtime compared with methods that require a post-OPC mask. We also showed the importance of lithography domain knowledge to train ML for hotspot detection.

Aim: To investigate methods that can be used to predict and manage the effects of manufacturing variability on product quality during the design process. Methodology: The preliminary investigation is a review and analysis of probabilistic methods and quality metrics. Based on this analysis, convenient robustification methods are developed. In addition, the nature of the flow of variability in a system is considered. This is then used to ascertain the information needed for an input variable when predicting the quality of a proposed design. The second, and major, part of the investigation is a case-by-case analysis of a collection of manufacturing operations and material properties. Each is initially analysed from first principles. On completion, the fundamental causes of variability of the key characteristic(s) are identified. Where possible, the expected variability for each of those characteristics has been determined. Where this determination was not possible, qualitative conclusions about the variability are made instead. In each case, findings on the prediction and management of manufacturing variability are made.

Improvement of manufacturing productivity is dependent on the successful automation of manufacturing processes, the success of which is based in turn upon the availability of information which describes the state of manufacturing operations. Acoustic Emission (AE) signals related to the cutting process and tool wear have been recently applied to monitor manufacturing processes, and various AE parameters can be used to provide process information. For example, when cutting tools become worn, AE energy generated at the interface of tool flank and work piece increases. This study is thus an experimental investigation of the AE spectrums representing AE signals energy distribution to determine the possibility of extracting useful parameters to provide on-line information about drilled-hole quality and drill-bit wear. An experiment conducted using a radial-arm drilling machine was employed to collect on-line AE drilling process spectrums, yielding eight indicator parameters. Drill wear states were measured using a machine vision system. Assessment of the drilled hole quality was based on tolerances established in Geometric Dimensioning and Tolerancing (GD&T). Correlations among drill wear, drilled-hole quality measurements, and the AE spectrum indicator parameters were examined by regression analysis. A forward-stepwise variable selection procedure was used to select the best-fit regression model for each drilled hole quality measurement associated with the set of one AE parameter raised to different powers. According to quality measurements, drilled holes were categorized as either "acceptable" or "unacceptable" holes, using cluster analysis with a group-averaging method. The usage of AE parameters to decide to which group a drilled hole belonged was also examined. From the experimental evidence, it was observed that there are strong relationships between AE parameters and drill-wear state and the quality measurements of drilled holes. AE parameters could be useful predictor variables to provide information to controller/operators to evaluate current drilling processes. Based on the status information of drill wear and the quality measurements, drilling processes can be adjusted accordingly. / Graduation date: 1997

The importance of integrating design and manufacturing becomes apparent when the increase in the degree of difficulty of change is observed as the product development proceeds from concept to production in a serial engineering process. The greatest opportunity in design for manufacture occurs at the initial design stage before any commitments to tooling and equipment have been made. This research develops a framework and an implementation system dealing with integration of design, manufacturing and economic aspects in the development of a product. The objective is to evaluate process technology for a specified product design and to identify the best work/tool material combination and production conditions to optimize the production process. A commercial CAD/CAM package (SmartCAM) playing roles as a part design tool, a processing time simulator, and a NC code generator is integrated with a manufacturing database, and a machining cost model. This integrated system runs in Microsoft Windows environment under an external program which not only coordinates the activities of various modules but also enhances the capabilities of SmartCAM. This system allows product design evaluation for economic and technical criteria and recommends best manufacturing environment. An NC program containing recommended machining parameters is generated. Furthermore, the system reports on tool wear on each tool per part manufactured. This information is useful for cost analysis as well as for producing a tool replacement schedule. / Graduation date: 1995

by Stanley B. Gershwin ... [et al.]. / "September, 1984." / Bibliography: leaves 14-17.

Originally presented as the author's thesis--Technische Universität Darmstadt, 2007. / Includes bibliographical references.

Thesis (M.S.)--Mississippi State University. Department of Industrial Engineering. / Title from title screen. Includes bibliographical references.

Thesis (M.S.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains xi, 85 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 53).