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Period 3: Operations Period, 95 96, 99 shortening, 125 127 tailoring (steps/techniques), 122 125 technical aspect, 99, 102 108 development tactics, 116 119 modeling, 104 108 systems engineering and, 102 104 technology insertion, 119 120 templates (Figure 7.2), 87 in Wheel and Axle Model (Figure 3.3), 24 Project Estimate Phase, Eight Phase Estimating Process, 418 419 Project Information Center, 80, 282 283 Project leadership, 24, 133 134, 185, 290 291, 319 337 inf luence categories, 321 management versus leadership, 320 motivational techniques, 322 333 principles (Useem), 319 project manager, 185 (see also Project manager) right-brain activity, 320 styles, 329, 333 337 visibility and, 290 291 vision and, 320 323 Project management. See also Project manager: adversarial, 69 defined, 6 macro level, 225 survey on perception of importance of, 390 systems engineering, interdependency with, 6 7 Project Management Institute (PMI), 15, 16, 403, 404 certification, 16, 41, 404 Organizational Project Management Maturity Model (OPM3), 387 overview table, 404 Project Management Body Of Knowledge (PMBOK Guide), 2, 12, 20, 36, 404 Project Management Professional (PMP), 16, 41, 404 Project manager: accountability, 184 authority, 46 47, 183 184, 187 189 as buyer of services provided by support managers, 195 competency model (Table 11.1), 185, 186 leadership, and personal factors, 321 operating style, 334 organization options and, 168 169 professional certification of, 16 responsibilities, 77, 168 169, 183 184 selecting, 185 187 technique versus styles, 334 weekly review, 287 288 Project network, 22, 126, 200, 208 214 Project office triad, 190 Project opportunity cycle, 30 Project organization, pure, 171 172. See also Organization options

properties are studied and compared with the desired values These values may be readily measured quantities, such as the coercive eld of a ferroelectric lm or the dielectric constant of a lm intended for capacitor applications Alternatively, they may be more subjective measures, such as microstructural quality, which may be quanti ed by the assignment of a numerical scale As shown by the italicized solution preparation parameters in Table 26, the key processing parameters can be determined These signi cant factors may then be subsequently analyzed by a variety of approaches In the present case, a uniform shell design (see Figure 9 in Ref 114) was used to determine sweet spots in processing space that produce lms with optimum properties and that may later be used routinely for solution synthesis and lm fabrication.

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These statistical methods, although providing less fundamental insight into the cause effect relationships associated with lm processing, can prove powerful The lower half of Table 26, which is reproduced from the work of Lockwood et al,114 clearly demonstrates the utility of the method The coercive eld was reduced at the sweet spot condition by 40% compared with the value obtained under the standard/original processing conditions Given the large number of variables inherent in the process, it is perhaps surprising that the basic statistical design of experiments approach has not been employed more widely Researchers who have focused more on understanding cause effect relationships in solution processing have given attention to lm drying and pyrolysis behavior, densi cation processes, and nucleation and growth into the desired crystalline state Both thermodynamic and kinetic factors associated with the phase transformation from the amorphous state to the crystalline state have been considered.

116 119 Control of these factors can lead to improvements in the ability to in uence the microstructure It is noted that in the previous sentence, in uence has been carefully chosen, since the ability to manipulate the factors that govern the nature of the phase transformation to the extent that full control of the microstructure is possible remains to be demonstrated However, trends in characteristics such as lm orientation and columnar versus uniaxial grains have certainly already been achieved120 A key area of focus for understanding the thermodynamic aspects of the phase transformation has been on consideration of thermodynamic driving forces associated with the phase transformation from the amorphous to the crystalline state.

106,118,119 As sol-gel processes were developed in the late 1970s to late 1980s, there was signi cant attention given to the nature of chemically derived amorphous materials and whether they were truly different from amorphous materials prepared from oxide precursors1,63,105 Distinguishing characteristics of chemically derived materials, which may be reduced or eliminated compared with the oxide-derived glass based on heat treatment conditions, include residual hydroxyl content, surface area associated with nanoporosity in the material, and skeletal density1,63,67,71,105 The role of these factors as a thermodynamic driving force are shown in Fig 21364,121 This gure suggests that the driving force associated with the nucleation of the crystalline.

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