the parents of the next generation. For this purpose, there are fitness-proportionate techniques such as Weighted Roulette Wheel Selection (Weighted RWS) and Stochas- tic Universal Sampling (SUS). These methods make sure that, if a chromosome has a strong fitness, it will have proportionately high probability of reproducing. Moreover, we make sure that a (small) proportion of the fittest chromosomes pass directly to the next generation. This action is called elitism and its purpose is to prevent loosing the few best found solutions, increasing the performance of the genetic algorithm. The process of combining two chromosomes is called crossover. Every time, two offsprings are produced by two parents and the parents are replaced. The first offspring takes a part of each parent while the other obtains the remaining part of the parents. We want our genetic algorithm to avoid falling in local optima. Thus, the concept of mutation is applied on the chromosomes after the crossover process. Mutation changes the new offspring by altering, with a small probability, the value of their genes increasing the chance for reaching to the global optimum. The process of generating new populations terminates, usually, when ∼ 90% of the chromosomes have the same fitness value or the highest ranking solution’s fitness has reached a plateau, i.e. successive iterations no longer produce better results. Alternatively termination occurs in the case number of generations is greater than a certain limit.
This file contains all of the numerical data that the .measure statements recorded; for each clock sink there is a rising insertion delay, falling insertion delay, rising transition time, and a falling transition time. This mt0 file is represented as “Raw Simulation Data” in figure 3-1. Important clock tree performance numbers can then be extracted from the “Raw Simulation Data”. This is performed in the next step of the flow, the “Find Skew and Slew Rates” step. This step is also shown in blue because it is automated by the supplied find_skew Perl script. This code parses the mt0 file and records the maximum and minimum rising and falling insertion delay. From this, the rising and falling skews are calculated. Normally we would only care about one of these values unless there are both rising and falling edge triggered storage elements in the same design, or latches and flip-flops in the same design. The script also finds the maximum rising and falling slew. All of this information is printed to the standard output, but can be redirected to a file. It is recommended that the user run this circuit simulation methodology once for each tier and once for the design as a whole also as a check. The performance statistics for the whole design should make sense in the context of the results of the tiers individually, for example by looking at the maximum and minimum insertion delays of the individual tier runs, we should be able to roughly predict the skew of the design as a whole. This information is the goal of the clock tree circuit simulations from figure 2-6; we will find the same information and more from a static timing tool next, and check that the methods agree with one another.
Advancement in the field of very large scale integration (VLSI) have lead to a decrease in device geometries (deep submicron technology), high device densities, high clock rates, and thus small signal transition times. Thus, interconnection lines that were once considered to be electrically isolated can now interfere with each other and have an important impact on system performance and correctness. One such interaction caused by parasitic coupling between wires is known as crosstalk. If not carefully considered during design validation, crosstalk can cause extra signal delay, logic hazards, and even circuit malfunction. Accurate modeling and simulation of interconnect delay due to crosstalk thus becomes increasingly important in the design of high-performanceintegratedcircuits.
Such an EH consists of components from several physical domains, including mechanical, magnetic and electrical, as well as external circuits which regulate and store the gen- erated energy. Therefore performanceoptimization should be based on a model that describes the EH as an integrated system. However, the thrust of the research eﬀorts in EH at present has focused on eﬃcient design of either the micro generators  or the circuit boosters  separately. There has been little reported research on systematic modeling and optimization of EH so the aim of this paper is to pro- pose such an approach. Some reported circuit designs treat the micro-generator as an ideal voltage source  but we show that modeling the micro generators using ideal voltage source correlates poorly with practice. Other reported de- signs use a simple linear equivalent circuit model  but we also show that simulation results from such a model are not accurate. Based on this motivation, we developed a mixed physical-domain behavioral model. Mixed-technology HDL modeling itself is not new, but what is new here is that HDL provides accurate modeling technique for EH. Also, enhanced performance of EH is achieved through the use of HDL-based optimization. Several HDLs that support mul- tiple domain system modeling and simulation are available, such as VHDL-AMS, Verilog-AMS and SystemC-A. In this paper VHDL-AMS  has been chosen as the modeling lan- guage.
in detail the sensitivities of all the network components with respect to a number of technology parameters using the Green’s function expressions as before. The sensitiv- ity analysis as mentioned has quite a few advantages. It allows the evaluation of the impact of slight imperfections in the fabrication process on the circuit’s performance and, ultimately, its yield. It also can be used as a quality factor for the selection of the best cost-effective technology on the basis of a class of circuits one want to fabricate with given specifications. Further, the analysis can be used during optimization to help the decision process providing guidance to the best possible improvement. The authors conclude that the effects of technology migration/scaling can be carried out efficiently for a given chip without the need of performing a large number of complete substrate extractions. The paper gives a detailed account of the algorithm underly- ing the sensitivity analysis based optimization technique. Also a number of design optimization problems are presented and the suitability of the approach is explained for a specific mixed-mode IC designed with substrate-aware optimization technique in CMOS process.
and CPU 2, which reside on tier A and tier C, respectively. Using the same reasoning for partitioning the CPUs to tier B, the data memory controller was partitioned to tier B to enable SRAM connectivity directly above and below it. For balance issues, the data SRAMs were divided evenly among tier A and tier C. This leaves just the instruction memory controller and the instruction SRAM, which were both delegated to tier B in the interest of balance once again. Figure 6-4 summarizes the complete partitioning scheme of the test case. In this diagram, the thick blue lines crossing the horizontal red dashed line represent the communication paths utilizing 3D vias. This 3D organization is attractive from a performance standpoint since the CPUs are “sandwiched” between the memory hierarchies with which they constantly communicate. Moreover, the data memory controller is theoretically residing only the length of a 3D via away from the data SRAMs. This would not be possible with 2D partitioning, as there would undoubtedly need to be trade-offs between the lengths of the busses.
The optimization process of engineering problems in many ﬁelds of science is a complex task since the interaction between multiple physical parameters and not trivial boundary conditions aﬀects the structure of the objective function and related algorithm convergence. In order to face this complexity, the authors introduced a new algorithm called SNO, ﬁrst presented in  in a simplistic way and further developed to enhance its performance, essentially built as a population based algorithm inspired to the social network knowledge sharing and emulating the decision making process recently introduced by these networks.
copper diffuses into gold, which deteriorates the performance . Therefore, it is necessary to insert a barrier between copper and gold to isolate the copper so that gold can be directly plated on the top of barrier layer in order to reduce the limitations of the sputtering and vacuum evaporation processes . In addition, barriers must not only be successful in retarding the copper diffusion but also have good adhesion performance, solderability, and bondability. Electroless nickel immersion gold finish is a widely used process in many industries, including microelectronics and printed circuit board manufacturing to protect the copper circuit from oxidation . This process is simple and requires fewer chemicals and less machine maintenance. Without any metal waste, both sides of the substrate can be coated in one operation. Thus, total time required to coat a substrate is considerably less than that in the sputtering and vacuum evaporation processes.
Photonic integratedcircuits (PICs) integrate multiple photonic functional ele- ments (such as waveguides , lasers , detectors , modulators , etc.   ) to perform a wide variety of advanced optical functions. Progress in other fields such as plasmonics , meta-materials , etc. has also contributed to the development of PICs. However, there are still some basic issues to be solved with optimization method, instead of using complicated fabrication or intensive parameter search.
This thesis will outline the creation of a tool set with the capability to simu- late spatial power combining systems. This thesis documents the development of a tool flow for integrated steady state nonlinear circuit analysis and electromagnetic analysis of a quasi-optical grid amplifier system. The analysis incorporates surface modes, nonuniform excitation, and full nonlinear effects. The work is verified using measurement of a 5x5 grid amplifier.
Commonly, an LDO is specified, designed and verified for DC load currents. In contrast, a digital load creates large cur- rent spikes. As an LDO designed for low quiescent current is too slow to react on fast current spikes, a minimum on-chip capacitance is required to keep the supply voltage within a certain error window. Different fully-integrated LDO topolo- gies are investigated regarding their suitability to supply low- voltage digital circuits. The any-load stable LDO topology is selected and implemented on a 0.13 µm test-chip. The LDO is able to provide a maximum load current of 2.5mA while consuming a quiescent current of 17 µA.
In this paper authors have presented a new approach to improve the performance of the glitch free cascadable adiabatic logic (GFCAL) circuit by replacing the triangular power supply with sinusoidal and trapezoidal power supplies (that control the charging and discharging of the capacitive load) and by sizing of transistors. A simulative investigation and performance analysis of proposed approach based 3 bit GFCAL counter, GFCAL JK flip flop and GFCAL 6T-SRAM circuit have also been done. The triangular power supply produces very large delay at the outputs of GFCAL circuits thus it will be very difficult to cascade larger circuits. A solution to provide cascadability is optimization of the delay. In the proposed approach the delay of GFCAL counter for triangular supply has been improved about 40% and 60% whereas for JK flip flop it is 46% and 49% and for 6T SRAM it is 17% and 91% with sinusoidal and trapezoidal power clocks respectively.
In this project we design low cost high performance programmable home security system using few LDR’s as an input sensors. When above sensor(s) get triggered system may dial the user specified phone number (using build-in DTMF generator) and activate the high power audio alarm and lights. All the
Modem technology has however shown an increasing trend towards faster circuits, shorter rise times, and smaller pulse widths (Schutt-Aine and Mittra 1989, Deutsch et al 1990). At the same time, the physical dimensions of modern integratedcircuits and packages are becoming ever increasingly smaller (Ruehli 1979, Deutsch et al 1990). This technology has resulted in very high speeds such that the behaviour of the interconnections can no longer be neglected. These integratedcircuits now have delay times that are comparable with the delays of the interconnections used in packaging these circuits. This means that the transmission line properties of these interconnects need to be taken into account.
The QCA logic has roots in the majority gates, majority data structures, and majority functions. The research in developing methods for implementation of majority functions and CAD tools for QCA is quite immature . The genetic algorithm (GA) approach in designing and optimization of conventional digital and analog circuits has been the subject of research in the last few decades [5,6]. The advantages of GAs over traditional algorithms are that they are intrinsically parallel, there is a high chance of getting an optimal solution, and a wide range of solutions is possible. In this paper, we review previous works on representation and implementation of majority functions and utilize a genetic algorithm to reduce the number of majority gates in implementation of any three-variable function. The remainder of this article is organized as follows. Section 2 presents QCA paradigm and corresponding logic gates in brief. We review the methods of recognizing majority functions and contribute a new approach based on geometric representation. A brief review of majority logic implementation and reduction methods based on the K- map, three-cube, and GA is given in section 3. Our proposed method, the two-level mapping based on GA, is presented in section 4. We describe and discuss the results in section 5. Finally, section 6 gives the conclusions.
SGSG configuration between the two signal Vias have been simulated. As expected, if the distance between the two signal 3D Vias is larger, the crosstalk level is going down as shown in Figure 5. The effect of the distance of GND Vias with respect to the signal via on the crosstalk is also evaluated as shown in Figure 6 with four distances: 4 µm, 8 µm, 16 µm, and 32 µm. The results show an increase in the magnitude of the crosstalk as the distance of the reference via (GND) increases. Also it is shown in Figure 7 that the crosstalk magnitude of SGGS (i.e. the cross locations of the signal and ground Vias) configuration is smaller than that of SGSG configuration comparing two same distance cases. The difference of the two cases is almost 10dB. This is a very interesting point for 3D designers to keep in mind, because just changing the 3D Via role can reduce the crosstalk magnitude especially in high frequency applications, where crosstalk problem is very critical to obtain the maximum system performance.