Design rule constraints

Figure 4.1 shows the two types of synthesis constraints and the related dc shell commands. Optimization constraints are user specified constraints. The two optimization constraints are speed and area constraints. In other words, DC considers speed and area as the two criteria for optimization. In addition to optimization constraints, the synthesis tool is required to meet another set of constraints called Design Rule Constraints (DRC). DRC are constraints imposed upon the design by requirements specified in the target technology library. Thus, DRC have precedence over optimization constraints since DRCs have to be met in order to realize a functional design. 

The three design rule constraints are max fanout, max transition and max capacitance. In this section, we introduce these terms and the related dc shell conunands to specify these constraints. 

The max transition design rule constraint does not provide a direct control over the actual capacitance of nets. The max.capacitance design rule constraint was introduced to provide a means to Umit capacitance directly. This constraint behaves like the max.transition constraint, but the cost hmction is based on the total capacitance of the net instead of the transition time. The max capacitance constraint is fully independent, so one can use it in conjunction with max.transition. During compile, DC ensures that there are no max.capacitance violations, that is, the max.capacitance constraint on the output pin of a driving cell exceeds or equals the summation of the capacitance of the pins driven by this cell and the net capacitances. The max capacitance attribute can be specified on designs or ports, max.transition, max fanout and max.capacitance can be used to control buffering in a design. 

Figure 22.9 shows examples of acceptable and imacceptable synthesized fingerprints, since not all compositions produce useful macroprimitives, even if the topological constraints and design rules are apphed. 

Equation (c) would be used in the design of the filter, hence Equation (c) imposes a constraint that must be taken into account. The optimal solution becomes Vf = 940 gal/ft2 and Q = 14.2 gal/(min)(ft2) with Equation (c) included in the problem (see Figure E13.3). A rule of thumb is 2 gal/(min)(ft2) (Letterman, 1980), as compared with the optimal value of Q. 

Using the field model described in section 1, detection probabilities are to be computed for each grid point to find the breach probability. The optimal decision rule that maximizes the detection probability subject to a maximum allowable false alarm rate a is given by the Neyman-Pearson formulation [20]. Two hypotheses that represent the presence and absence of a target are set up. The Neyman-Pearson (NP) detector computes the likelihood ratio of the respective probability density functions, and compares it against a threshold which is designed such that a specified false alarm constraint is satisfied. 

If the value of VERTICAL.TUBE.ROTORS is found to be true (or "yes") then clause 2 of the rule is evaluated. The references to "fact" in clause 2 cause the system to refer to a table that contains the facts for particular rotors. References to the facts ROTOR.DESIGN, TUBE.VOLUME, and K.FACTOR are applications of particular constraints to the rotors. For example, two constraints are that the rotor must have a tube volume greater than 1 mL and a k factor less than 50. Clause 3 further pares the set of rotors on the basis of k factor by taking only the best rotor and any rotor with a k factor within 50% of the k factor of the best rotor. 

What does all of the above analysis teach us First and above ail, the correct LR behavior at the FEG limit is vital for design of a good EDF. Second, proper sum rules should be satisfied to build in systematic error cancellation. Third, the introduction of a weight function releases the constraints on the original formulas at the FEG limit, allows any nonlocal effects to be modeled, and somewhat more importantly, provides a new degree of freedom so that other restrictions can be simultaneously satisfied. Fourth, any recursion should be avoided to permit more efficient implementation. This in turn calls for a better understanding of the TBFWV. Finally, the O(M ) numerical barrier must be overcome so that any general application will be possible. 

Equations (3.3) and (3.4) have become known respectively as the valence sum rule and the loop, or equal valence, rule, and are known collectively as the network equations. Equation (3.4) represents the condition that each atom distributes its valence equally among its bonds subject to the constraints of eqn (3.3) as shown in the appendix to Brown (1992a). The two network equations provide sufficient constraints to determine all the bond valences, given a knowledge of the bond graph and the valences of the atoms. The solutions of the network equations are called the theoretical bond valences and are designated by the lower case letter 5. Methods for solving the network equations are described in Appendix 3. ... 

Problem formulations [ 1-3 ] for designing lead-generation library under different constraints belong to a class of combinatorial resource allocation problems, which have been widely studied. They arise in many different applications such as minimum distortion problems in data compression (11), facility location problems (12), optimal quadrature rules and discretization of partial differential equations (13), locational optimization problems in control theory (9), pattern recognition (14), and neural networks... 

These rules do not apply strictly, but provide useful guidelines for synthesis design. The rules are generally not applicable to electrocydic reactions or to substrates containing non-second-period elements (e.g. P or S), because their longer bond lengths imply different geometric constraints. 

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