Optimization of the Electrical Performance of Polymeric Films

REFERENCES

• Liu, Y.; Cui, T.; Varahramyan, K. Fabrication and characteristics of polymeric thin-film capacitor. Solid State Electronics 2003, 47, 811–814.
   Web of Science ®Google Scholar
• Misra, S.C.K.; Yadav, M.; Mathur, P. Non-linear dielectric properties of vacuum deposited nanocrystalline polyaniline thin films. Thin Solid Films 2006, 514, 272–277.
   Web of Science ®Google Scholar
• Kubat, J.; Kuzel, R.; Krivka, I.; Bentsson, P.; Prokes, J.; Stefan, O. New conductive polymeric systems. Synthetic Metals 1993, 54, 187–194.
   Web of Science ®Google Scholar
• Prosposito, P.; Matteis, F. D. Advances in Macromolecules; Macromolecular Systems with Nonlinear Optical Properties: Optical Characterization and Devices; Rossu: Rome, M.V. Edition, 2010; 119 pp.
   Google Scholar
• Bai, F.; Jones, T.D.; Kelley, T.W.; Lee, T.; Lewandowski, K.M.; Muyres, D.V. Organic Polymers, Laminates and Capacitors; Patent Number WO 2004102690 A2, 2004.
   Google Scholar
• Gudesen, H.G.; Nordal, P. A Memory Cell; Patent Number EP 1481398 A1, 2004.
   Google Scholar
• Alcatel; Vzw, I.M.C. Method of Transferring Ultra Thin Substrates and Application of the Method to the Manufacture of a Multilayer Thin Film Device; Patent Number EP 1041624 A1, 2000.
   Google Scholar
• Kumar, R.; Singh, S.; Misra, A.K. Development of NO2 gas sensor based on plasma polymerized nanostructure polyaniline thin film. Journal of Minerals & Materials Characterization & Engineering 2010, 9, 997–1006.
   Google Scholar
• Kroll, K.C.; Kroll, M.W. Implantable Defibrillator Employing Polymer Thin Film Capacitors; Patent Number EP 0739224 A1, 1996.
   Google Scholar
• Binder, M.; Lavene, B.; Mammone, R.J. Polymer Based Film Capacitor with Increased Dielectric Breakdown Strengths; Patent Number WO 1993014881 A1, 1993.
   Google Scholar
• Brabec, C.J.; Dyakonov, V.; Sariciftci, N.S.; Graupner, W.; Leising, G.; Hummelen, J.C. Investigation of photoexcitations of conjugated polymer/fullerene composites embedded in conventional polymers. Journal of Chemical Physics 1998, 109, 1185–1195.
   Web of Science ®Google Scholar
• Aydogan, S.; Saglam, M.; Turut, A. Characterization of capacitance–frequency features of Sn/polypyrrole/n-Si structure as a function of temperature. Polymer 2005, 46, 6148–6153.
   Web of Science ®Google Scholar
• Morales-Acosta, M.D.; Alvarado-Beltrán, C.G.; Quevedo-López, M.A.; Gnade, B.E.; Mendoza-Galván, A.; Ramírez-Bon, R. Adjustable structural, optical and dielectric characteristics in sol–gel PMMA–SiO2 hybrid films. Journal of Non-Crystalline Solids 2013, 362, 124–135.
   Web of Science ®Google Scholar
• Baraskar, S.S.; Banwait, S.S.; Laroiya, S.C. Multiobjective optimization of electrical discharge machining process using a hybrid method. Materials & Manufacturing Process 2013, 28, 348–354.
   Web of Science ®Google Scholar
• Jourdan, L.; Schutze, O.; Legrand, T.; Talbi, E.-G.; Wojkiewicz, J.L. An analysis of the effect of multiple layers in the multi-objective design of conducting polymer composites. Materials & Manufacturing Process 2009, 24, 350–357.
   Web of Science ®Google Scholar
• Chakraborti, N.; Shekhara, A.; Singhala, A.; Chakraborty, S.; Chowdhury, S.; Sripriya, R. Fluid flow in hydrocyclones optimized through multi-objective genetic algorithms. Materials and Manufacturing Processes 2008, 16, 1023–1046.
   Google Scholar
• Giri, B.K.; Pettersson, F.; Saxén, H.; Chakraborti, N. Genetic programming evolved through bi-objective genetic algorithms applied to a blast furnace. Materials and Manufacturing Processes 2013, 28 (7), 776–782.
   Web of Science ®Google Scholar
• Li, Y.; Chen, Y.-Y.; Chen, C.Y.; Shen, C.-H.; Chang, H.-W.; Lo, I.-H.; Chen, C.-N. Device simulation based multiobjective evolutionary algorithm for process optimization of semiconductor solar cells. Materials & Manufacturing Process 2013, 28, 761–767.
   Web of Science ®Google Scholar
• Nepomuceno, E.G.; Takahashi, R.H.C.; Aguirre, L.A.; Neto, O.M.; Mendes, E.M.A.M. Multiobjective nonlinear system identification; a case study with thyristor controlled series capacitors. Materials & Manufacturing Process 2004, 35, 537–546.
   Google Scholar
• Coello, C.; Becerra, C.A.; Landa, R. Evolutionary multiobjective optimization in materials science and engineering. Materials and Manufacturing Processes 2009, 24 (2), 119–129.
   Web of Science ®Google Scholar
• Kasat, R.B.; Ray, A.K.; Gupta, S.K. Applications of genetic algorithms in polymer science & engineering. Materials and Manufacturing Processes 2003, 18, 523–532.
   Web of Science ®Google Scholar
• Mitra, M. Genetic algorithms in polymeric material production, design, processing and other applications: a review. International Materials Reviews 2008, 53, 275–297.
   Web of Science ®Google Scholar
• Pettersson, F.; Suh, C.; Saxen, H.; Rajan, K.; Chakraborti, N. Analyzing sparse data for nitride spinels using data mining. Neural networks and multiobjective genetic algorithms. Materials and Manufacturing Processes 2008, 24 (1), 2–9.
   Web of Science ®Google Scholar
• Biswas, A.; Chakraborti, N.; Sen, P.K. Multiobjective optimization of manganese recovery from Sea nodules using genetic algorithms. Materials and Manufacturing Processes 2008, 24 (1), 22–30.
   Web of Science ®Google Scholar
• Pettersson, F.; Biswas, A.; Sen, P.K.; Saxen, H.; Chakraborty, N. Analyzing leaching data for low-grade manganese ore using neural nets and multiobjective genetic algorithms. Materials and Manufacturing Processes 2009, 24 (3), 320–330.
   Web of Science ®Google Scholar
• Datta, S.; Pettersson, F.; Ganguly, S.; Saxén, H.; Chakraborti, N. Identification of factors governing mechanical properties of trip-aided steel using genetic algorithms and neural networks. Materials and Manufacturing Processes 2008, 23, 130–137.
   Web of Science ®Google Scholar
• Kumar, A.; Sahoo, D.; Chakraborty, S.; Chakraborti, N. Gas injection in steel-making vessels: coupling a fluid dynamic analysis with a genetic algorithms-based pareto-optimality. Materials and Manufacturing Processes 2005, 20 (3), 363–379.
   Web of Science ®Google Scholar
• Nandan, R.; Rai, R.; Jayakanth, R.; Moitra, S.; Chakraborti, N.; Mukhopadhyay, A. Regulating crown and flatness during hot rolling: a multiobjective optimization study using genetic algorithms. Materials and Manufacturing Processes 2005, 20 (3), 459–478.
   Web of Science ®Google Scholar
• Paszkowicz, W. Genetic algorithms, a nature-inspired tool: survey of applications in materials science and related fields. Materials and Manufacturing Processes 2009, 24 (2), 174–197.
   Web of Science ®Google Scholar
• Ciurana, J.; Arias, G.; Ozel, T. Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel. Materials and Manufacturing Processes 2009, 24 (3), 358–368.
   Web of Science ®Google Scholar
• Li, Y.; Tseng, Y.-H. Hybrid differential evolution and particle swarm optimization approach to surface-potential-based model parameters extraction nanoscale MOSFETs. Materials and Manufacturing Processes 2011, 26, 388–397.
   Web of Science ®Google Scholar
• Baumes, L.A.; Blansche, A.; Serna, P.; Tchougang, A.; Lachiche, N.; Collet, P.; Corma, A. Using genetic programming for an advanced performance assessment of industrially relevant heterogeneous catalysts. Materials and Manufacturing Processes 2009, 24 (3), 282–292.
   Web of Science ®Google Scholar
• Dimitriu, R.C.; Bhadeshia, H.K.D.H.; Fillon, C.; Poloni, C. Strength of ferritic steels: neural networks and genetic programming. Materials and Manufacturing Processes 2008, 24 (1), 10–15.
   Web of Science ®Google Scholar
• Mukherjee, R.; Chakraborty, S. Selection of EDM process parameters using biogeography-based optimization algorithm. Materials and Manufacturing Processes 2012, 27 (9), 954–962.
   Web of Science ®Google Scholar
• Feiring, A.E.; Wonchoba, E.R.; Arthur, S.D. Fluorinated poly(ether sulfone)s. Journal of Polymer Science Part A: Polymer Chemistry 1990, 28, 2809–2819.
   Web of Science ®Google Scholar
• Negi, Y.S.; Adhyapak, P.V.; Damkale, S.R.; Goyal, R.K.; Islam, M.; Aiyer, R.C. Preparation of novel optical-grade metanitroaniline and polymethylmethacrylate-coated single crystals and their optical properties. Materials Letters 2004, 58, 3929–3932.
   Web of Science ®Google Scholar
• Negi, Y.S.; Adhyapak, P.V. Development in polyaniline conducting polymers. Journal of Macromolecular Science, Part C: Polymer Reviews 2002, 42, 35–53.
   Web of Science ®Google Scholar
• Ballet, W.; Picard, I.; Verbiest, T.; Persoons, A.; Samyn, C. Chromophore-functionalized polyethersulfones with high poling stabilities of the nonlinear optical effect. Macromolecular Chemistry and Physics 2004, 205, 13–18.
   Web of Science ®Google Scholar
• Choi, J.; Aggarwal, M.D.; Wang, W.S. Binary organic single crystals for nonlinear optical application. Journal of the Korean Physical Society 1998, 32, 433–435.
   Google Scholar
• White, J.; Hulin, D.; Joffre, M.; Migus, A.; Antonetti, A.; Toussaere, E.; Hierle, R.; Zyss, J. Ultrabroadband second harmonic generation in organic and inorganic thin crystals. Applied Physics Letters 1994, 64, 264–266.
   Web of Science ®Google Scholar
• Srinivasan, K.; Kanimozhi, S. Effect of different solvents on the habit of meta-nitroaniline single crystals. Crystal Research and Technology 2010, 45, 611–618.
   Web of Science ®Google Scholar
• Holland, J.H. (1975) Adaptation in Natural and Artificial Systems; University of Michigan Press: Ann Arbor, MI; re-issued by MIT Press, 1992; pp. 55.
   Google Scholar
• Kennedy, J.; Eberhart, R. Particle swarm optimization. Proceedings IEEE International Conference Neural Network 1995, 4, 1942–1948.
   Google Scholar
• Storn, R.; Price, K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 1997, 11, 341–359.
   Web of Science ®Google Scholar
• Ali, M.; Pant, M.; Abraham, A. A modified differential evolution algorithm and its application to engineering problems. In Proceedings of International Conference of Soft Computing and Pattern Recognition 2009, Malacca, Malaysia, December 4–7, 2009; pp. 196–201.
   Google Scholar
• Baran, M.E.; Wu, F.F. Optimal sizing of capacitors placed on a radial distribution system. IEEE Trans Power Delivery 1998, 4 (1), 735–743.
   Web of Science ®Google Scholar
• Li, H.; Zhang, Q. Multi-objective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation 2009, 13 (2), 284–302.
   Web of Science ®Google Scholar
• Deb, K.; Thiele, L.; Laumanns, M. Scalable test problems for evolutionary multi-objective optimization, In Technical Report, Computer Engineering and Networks Lab (TIK), ETH Zurich, Switzerland, 2001.
   Google Scholar
• Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 2002, 6 (2), 182–197.
   Web of Science ®Google Scholar