Recipe Parameters

Another variable in CMP recipes is the back pressure. Usually, if the nonuniformity problem is identified to be due to a center-slow-edge-fast process, back pressure can be used to push the back of a wafer and accelerate the center polish rate. Thus, the uniformity can be improved accordingly. 

Trend chart showing nonuniformity in daily qualification of a CMP tool for a whole month. Three wafers were tested per day. 

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There are various uncertainties in all the data influencing the selection of a set of equipment uncertainties in recipe parameters, product specifications, processing times and size factors, equipment availability, product requirements, and resource availability. Data needed for the evaluation of processing times and equipment sizes are never 100% reliable. The market situation when the plant is started up will certainly be different from the situation at the time of the definition of a production program for the plant. Unpredictable process disturbances may also occur. 

Typically, there are three principal parameters in a CMP recipe, namely, the down force, the platen rotation speed, and the carrier rotation speed. If we put the results of the DOE study from Table I into contour plots, it is easier to see the influences of each factor on the polish nonuniformity. Figures 6-8 show the relationships between recipe parameters and the polish rate and nonuniformity. We see, from Fig. 6, that the polish rate and nonuniformity can be simultaneously improved by using a larger down force. However, if the down force is set too high, scratches on a wafer may result. Decreasing the platen speed and the carrier speed decreases nonuniformity, as we can see from Figs. 6 and 7. However, decrease of the platen speed also decreases the polish rate, as shown in Fig. 6. 

CMP processes have been developed, though for Ge/SiGe the CMP process is already in an advanced level. Overall the implementation of Ge/SiGe as high mobility channel material is already more mature compared to the activities on III—V materials. As only limited experimental CMP data are currently available, it is clear that these high mobility material CMP processes could stiU be improved with optimized consumables and recipe parameters. Whether or not this optimized process development will be needed remains to be seen, as it is not 100% sure yet that these high mobility materials will indeed be introduced into the <10 nm CMOS technology nodes. 

The term recipe has a range of definitions in batch processing, but in general a recipe is a procedure with the set of data, operations, and control steps required to manufacture a particular grade of product. A formula is the list of recipe parameters, which includes the raw materials, processing parameters, and product outputs. A recipe procedure has operations for both normal and abnormal conditions. Each operation contains resource requests for certain ingredients (and their amounts). The operations in the recipe can adjust set points and turn equipment on and off. The complete production run for a specific recipe is called a campaign (multiple batches). 

Although generalized correlations are based on data for pure fluids, they are frequently appHed to mixtures. The mole fraction is introduced as a variable through empirical recipes for the composition dependence of parameters upon which the correlation is based. The simplest such recipes provide pseudoparameters that are linear in mole fraction ... 

Although the Pitzer correlations are based on data for pure materials, they may also be used for the calculation of mixture properties. A set of recipes is required relating the parameters T, Pc, and (0 for a mixture to the pure-species values and to composition. One such set is given by Eqs. (2-80) through (2-82) in Sec. 2, which define pseudopa-rameters, so called because the defined values of T, Pc, and (0 have no physical significance for the mixture. 

Sometimes the term recipe is used to designate only the raw material amounts and other parameters to be used in manufacturing a batch. Although appropriate for some batch processes, this concept is far too restrictive For others. For some products, the differences from one product to the next are largely physical as opposed to chemical. For such products, the processing instruc tions are especially important. The term formula is more appropriate for the raw material amounts and other parameters, with recipe designating the formula and the processing instruc tions. 

The semibatch model GASPP is consistent with most of the data published by Wisseroth on gas phase propylene polymerization. The data are too scattered to make quantitative statements about the model discrepancies. There are essentially three catalysts used in his tests. These BASF catalysts are characterized by the parameters listed in Table I. The high solubles for BASF are expected at 80 C and without modifiers in the recipe. The fact that the BASF catalyst parameters are so similar to those evaluated earlier in slurry systems lends credence to the kinetic model. 

The next two steps after the development of a mathematical process model and before its implementation to "real life" applications, are to handle the numerical solution of the model s ode s and to estimate some unknown parameters. The computer program which handles the numerical solution of the present model has been written in a very general way. After inputing concentrations, flowrate data and reaction operating conditions, the user has the options to select from a variety of different modes of reactor operation (batch, semi-batch, single continuous, continuous train, CSTR-tube) or reactor startup conditions (seeded, unseeded, full or half-full of water or emulsion recipe and empty). Then, IMSL subroutine DCEAR handles the numerical integration of the ode s. Parameter estimation of the only two unknown parameters e and Dw has been described and is further discussed in (32). 

Ethylene-propylene-diene terpolymers (EPDM), with their inherent complexity in structural parameters, owe their tensile properties to specific structures dictated by polymerization conditions, among which the controlling factor is the catalyst used in preparing the polymers. However, no detailed studies on correlation between tensile properties and EPDM structures have been published (l,2). An unusual vulcanization behavior of EPDMs prepared with vanadium carboxylates (typified by Vr g, carboxylate of mixed acids of Ccj-Cq) has been recently reported Q). This EPDM attains target tensile properties in 18 and 12 minutes at vulcanization temperatures of 150 and l60°C respectively, while for EPDMs prepared with V0Cl -Et3Al2Cl or V(acac) -Et2AlCl, about 50 and 0 minutes are usually required at the respective vulcanization temperatures, all with dieyclopentadiene (DCPD) as the third monomer and with the same vulcanization recipe. This observation prompted us to inquire into the inherent structural factors... 

Elaboration of the method for the identification of colour compounds by RPLC MS should comprise four steps (1) spectral characterization of reference materials (standards) and subsequent optimization of detection parameters, as well as those of their chromatographic separation (2) analysis of natural dyestuffs used as colouring materials in historical objects (3) analysis of model samples (dyed fibres, paintings) prepared according to old recipes (4) application of the acquired knowledge to identification of colourants present in historical objects. 

Cholesterol s presence in liposome membranes has the effect of decreasing or even abolishing (at high cholesterol concentrations) the phase transition from the gel state to the fluid or liquid crystal state that occurs with increasing temperature. It also can modulate the permeability and fluidity of the associated membrane—increasing both parameters at temperatures below the phase transition point and decreasing both above the phase transition temperature. Most liposomal recipes include cholesterol as an integral component in membrane construction. 

The simulation module simulates the basic operation(s) which are processed by a combination of a vessel and a station using a discrete event simulator. All necessary data (basic operation(s), equipment parameters, recipe scaling percentage, etc.) is provided by the scheduling-module. The simulator calculates the processing times and the state changes of the contents of the vessels (mass, temperature, concentrations, etc.) that are relevant for logistic considerations. 

The structure of the recipes and the parameters of the unit operations (heating power, cooling power, mass flows, etc.) were taken from the process description and had to be slightly adapted during the first simulation runs. 

Comparison ofthe Plant Concepts To be able to compare the pipeless plant concept with the existing multipurpose batch plant, a reference plant was modelled using PPSiM. In the existing plant three conventional batch mixers work in a shifted parallel fashion. The three batch mixers were modelled by three stations and equipped with all technical functions necessary for the production of all recipes. Therefore each batch could be processed at one of the stations and the vessel transfers were limited to the transportation of empty or loaded vessels. All the other parameters of the model, e.g., charging mass flows, the durations of vessel cleanings and the recipes remained unchanged. 

The plant is used to produce two chemically different EPS -types A and B in five grain size fractions each from raw materials FI, F2, F3. The polymerization reactions exhibit a selectivity of less than 100% with respect to the grain size fractions Besides one main fraction, they yield significant amounts of the other four fractions as by-products. The production processes are defined by recipes which specify the EPS-type (A or B) and the grain size distribution. For each EPS-type, five recipes are available with the grain size distributions shown in Figure 7.2 (bottom). The recipes exhibit the same structure as shown in Figure 7.2 (top) in state-task-network-representation (states in circles, tasks in squares). They differ in the parameters, e.g., the amounts of raw materials, and in the temperature profiles of the polymerization reactions. 

All data objects contain an identification key and structured information. Materials and resources can be defined by relatively simple property tables. Master recipes require more complex structures to describe which resources have to be used at which time interval by which operation and with which operation parameters, and which materials are needed or produced at which point in time and in which quantity. 

Input product quantities are calculated based on the linear recipe function with the parameters ap and bPi on a tons per day basis. 

The influence of ZnCFO concentration (3,0 5,0 7,0 phr) on formation of properties complex of the unfilled rubber mixes and their vulcanizates on the basis of isoprene rubber of the following recipe, phr isoprene rubber - 100,0 sulfur - 1,0 di - (2-benzothiazolyl) -disulfide - 0,6 N, N -diphenylguanidine - 3,0 stearic acid - 1,0, was carried out in comparison with the known activator - zinc oxide (5,0 phr). The analysis of Rheometer data of sulfur vulcanization process of elastomeric compositions at 155°C (fig. 5) shows, that on crosslink density and cure rate, about what the constants of speed in the main period (k2) testify, they surpass the control composition with 5,0 phr of zinc oxide. Improvement of the complex of elastic - strong parameters of rubbers with ZnCFO as at normal test conditions, and after thermal air aging (tab. 1), probably, is caused by influence of the new activator on vulcanization network character. So, the percent part of polysulfide bonds (C-Sx-C) and amount of sulfur atoms appropriating to one crosslink (S atoms/crosslink) in vulcanizates with ZnCFO are decreased, the percent part of disulfide bonds (C-S2-C) is increased (fig. 62). 

The first task was to produce carriers from different recipes and in different shapes as shown schematically in Fig. 8. The raw materials diatomaceous earth, water and various binders are mixed to a paste, which is subsequently extruded through a shaped nozzle and cut off to wet pellets. The wet pellets are finally dried and heated in a furnace in an oxidising atmosphere (calcination). The nozzle geometry determines the cross section of the pellet (cf. Fig. 3) and the pellet length is controlled by adjusting the cut-off device. Important parameters in the extrusion process are the dry matter content and the viscosity of the paste. The pore volume distribution of the carriers is measured by Hg porosimetry, in which the penetration of Hg into the pores of the carrier is measured as a function of applied pressure, and the surface area is measured by the BET method, which is based on adsorption of nitrogen on the carrier surface [1]. 

Numerous carriers were produced from different recipes and in different sizes and shapes in a 1 kg/min lab scale extruder and subsequently calcinated under different conditions in a furnace. The recipes included different types of diatomaceous earth, different types and amounts of binder and variation of the water content of the paste, which is a critical extrusion parameter. The shapes included among others rings, multiple-holed rings, finned rings, and trilobes, whereas normal cylindrical pellets were not made due to their well-known inferior activity to pressure drop ratio. 

Before robustness testing is started, precise specification of all method parameters is certainly needed (Table 6) else it may not be clear later which method has been validated. Possible settings for initial CE experiments are shown in Diagram 2. In particular, it is important to specify the buffer composition, e.g., by buffer recipes (Table 8). Otherwise, inadvertent variations in pH or ionic strength can lead to variability in selectivity. 

The dependence of precision on different parameters has already been discussed. Precision is strongly dependent on the constancy of migration data. Thus, the stability of the EOF is most important. Buffer recipes describe clearly the preparation and avoid errors caused by, e.g., a poorly calibrated pH electrode. 

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