Load-independent characterization of trade-off fronts for operational amplifiers

Abstract—In emerging design methodologies for analog integrated circuits, the use of performance trade-off fronts, also known as Pareto fronts, is a keystone to overcome the limitations of the traditional top-down methodologies. However, most techniques reported so far to generate the front neglect the effect of the surrounding circuitry (such as the output load impedance) on the Pareto-front, thereby making it only valid for the context where the front was generated. This strongly limits its use in hierarchical analog synthesis because of the heavy dependence of key performances on the surrounding circuitry, but, more importantly, because this circuitry remains unknown until the synthesis process. We will address this problem by proposing a new technique to generate the trade-off fronts that is independent of the load that the circuit has to drive. This idea is exploited for a commonly used circuit, the operational amplifier, and experimental results show that this is a promising approach to solve the issue

Mathematical modeling and control of redundant robotic manipulators using biological analog

U ovom radu razmatran je problem realizacije novih matematičkih modela redudantnih sistema kao i upravljanja korišćenjem pogodnih bioloških analogona. Ideja je bila u tome da se imitira ljudsko ponašanje i to je posebno značajno za zadatke koji su slični onim zadacima karakterističnim za ljude. Prvo, može se primeniti biološki koncept distribuiranog pozicioniranja (DP) koji je zasnovan na inercijalnim osobinama i aktuatorskim mogućnostima zglobova redudantnih sistema. Drugo, predloženo je korišćenje biološkog analogona - sinergije koja je posledica postojanja invarijantnih osobina u izvršavanju funkcionalnih kretanja. Na kraju predloženo je korišćenje drugih bioloških principa kao što su: princip minimuma interakcije koji ima važnu ulogu u hijerarhijskoj strukturi upravljanja i principa samopodešavanja, koji dozvoljava efikasnu realizaciju upravljanja koje je zasnovano na iterativnom prirodnom učenju. .In this paper it is considered problem of realization new mathematical models of redundant systems as well as control using suitable biological analog. The idea was to try to imitate human behavior and this is specially convenient for tasks which are similar to those characteristic for humans (e.g., assembling in industry, different jobs at home and in health service). If we consider speed, accuracy and stability of motion then the overall performance (taking into account all three of parameters) with machines is still far behind human reaching and grasping. Human arm movements are considered to be stable, fast and accurate due to properties of muscles, musculo-skeletal structures and hierarchical control. It was observed in the execution of functional motions that certain trajectories are preferred from the infinite number of options. Such behavior of organisms can be only explained by the existence of inherent optimization laws in self-organized systems governing the acquisition of motor skills. Existence of invariant features in the execution of functional motions points out that central nervous system (CNS) uses synergy [Bernstein, 1967](i.e. rule(s) that can be developed by the CNS based on some principles). The control of arm movement in humans relies very much on distributed usage of different joints, and inherent optimization of muscles which are active. Analysis of multi joint coordination in humans is an important source of information for synthesis of dynamic patterns in machines. In that way, model of redundant system is obtained using biomechanical principle - synergy i.e. introducing linear or nonlinear relations between independent parameters or their first derivatives which uniquely define redundant system. Moreover, one can introduced hypothetical control using joint actuator synergy approach as suggested [Bernstein, 1967] •which imposes a specific constraints) on the control variables. Also, it can be applied biological concept called distributed positioning (DP) which is based on the inertial properties and actuation capabilities of joints of redundant system. The redundancy and DP concept [Potkonjak 1990] could be used for solving the trajectory that has problems with increased dynamic requirements. The concept of DP allows us to separate the smooth and accelerated components of required motions applying appropriate smoothing technique. The inverse kinematics of redundant robot has been solved at the coordination level via (DP) concept. Moreover, it is here proposed using other biological principles such as: principle of minimum interaction which takes a main role in hierarchical structure of control and self-adjusting principle, which allows efficiently realization of control based on iterative natural learning. Motor control is organized as a multilevel structure, is generally accepted. In assistive system involves man as the decision maker, a hierarchical control structure can be proposed with three levels from the left to right: -voluntary level, coordination level, actuator level. This imposes the system is decomposed into several sybsystems with strong coupling between subsystems. Explanation of previous can be understood using the principle of maximum autonomy or minimum information exchange [Tomović, Bellman, 1970]. According to this principle, the optimal solution is to delegate the execution of functional motions to the coordination level and local regulators once the task and the task parameters have been selected. Learning control for controlling dynamics systems, a class of tracking systems is applied where it is required to repeat a given task to desired precision. The common observation that human beings can learn perfect skills trough repeated trials motivations the idea of iterative learning control for systems performing repetitive tasks. Therefore, iterative learning control requires less a priori knowledge about the controlled system in the controller design phase and also less computational effort than many other kinds of control. For improving the properties of tracking is proposed applying biological analog -principle of self-adaptability, [Grujuc,1989 ] which introduce local negative feedback on control with great amplifying.

Mathematical modeling and control of redundant robotic manipulators using biological analog

U ovom radu razmatran je problem realizacije novih matematičkih modela redudantnih sistema kao i upravljanja korišćenjem pogodnih bioloških analogona. Ideja je bila u tome da se imitira ljudsko ponašanje i to je posebno značajno za zadatke koji su slični onim zadacima karakterističnim za ljude. Prvo, može se primeniti biološki koncept distribuiranog pozicioniranja (DP) koji je zasnovan na inercijalnim osobinama i aktuatorskim mogućnostima zglobova redudantnih sistema. Drugo, predloženo je korišćenje biološkog analogona - sinergije koja je posledica postojanja invarijantnih osobina u izvršavanju funkcionalnih kretanja. Na kraju predloženo je korišćenje drugih bioloških principa kao što su: princip minimuma interakcije koji ima važnu ulogu u hijerarhijskoj strukturi upravljanja i principa samopodešavanja, koji dozvoljava efikasnu realizaciju upravljanja koje je zasnovano na iterativnom prirodnom učenju. .In this paper it is considered problem of realization new mathematical models of redundant systems as well as control using suitable biological analog. The idea was to try to imitate human behavior and this is specially convenient for tasks which are similar to those characteristic for humans (e.g., assembling in industry, different jobs at home and in health service). If we consider speed, accuracy and stability of motion then the overall performance (taking into account all three of parameters) with machines is still far behind human reaching and grasping. Human arm movements are considered to be stable, fast and accurate due to properties of muscles, musculo-skeletal structures and hierarchical control. It was observed in the execution of functional motions that certain trajectories are preferred from the infinite number of options. Such behavior of organisms can be only explained by the existence of inherent optimization laws in self-organized systems governing the acquisition of motor skills. Existence of invariant features in the execution of functional motions points out that central nervous system (CNS) uses synergy [Bernstein, 1967](i.e. rule(s) that can be developed by the CNS based on some principles). The control of arm movement in humans relies very much on distributed usage of different joints, and inherent optimization of muscles which are active. Analysis of multi joint coordination in humans is an important source of information for synthesis of dynamic patterns in machines. In that way, model of redundant system is obtained using biomechanical principle - synergy i.e. introducing linear or nonlinear relations between independent parameters or their first derivatives which uniquely define redundant system. Moreover, one can introduced hypothetical control using joint actuator synergy approach as suggested [Bernstein, 1967] •which imposes a specific constraints) on the control variables. Also, it can be applied biological concept called distributed positioning (DP) which is based on the inertial properties and actuation capabilities of joints of redundant system. The redundancy and DP concept [Potkonjak 1990] could be used for solving the trajectory that has problems with increased dynamic requirements. The concept of DP allows us to separate the smooth and accelerated components of required motions applying appropriate smoothing technique. The inverse kinematics of redundant robot has been solved at the coordination level via (DP) concept. Moreover, it is here proposed using other biological principles such as: principle of minimum interaction which takes a main role in hierarchical structure of control and self-adjusting principle, which allows efficiently realization of control based on iterative natural learning. Motor control is organized as a multilevel structure, is generally accepted. In assistive system involves man as the decision maker, a hierarchical control structure can be proposed with three levels from the left to right: -voluntary level, coordination level, actuator level. This imposes the system is decomposed into several sybsystems with strong coupling between subsystems. Explanation of previous can be understood using the principle of maximum autonomy or minimum information exchange [Tomović, Bellman, 1970]. According to this principle, the optimal solution is to delegate the execution of functional motions to the coordination level and local regulators once the task and the task parameters have been selected. Learning control for controlling dynamics systems, a class of tracking systems is applied where it is required to repeat a given task to desired precision. The common observation that human beings can learn perfect skills trough repeated trials motivations the idea of iterative learning control for systems performing repetitive tasks. Therefore, iterative learning control requires less a priori knowledge about the controlled system in the controller design phase and also less computational effort than many other kinds of control. For improving the properties of tracking is proposed applying biological analog -principle of self-adaptability, [Grujuc,1989 ] which introduce local negative feedback on control with great amplifying.

System level performance and yield optimisation for analogue integrated circuits

Advances in silicon technology over the last decade have led to increased integration of analogue and digital functional blocks onto the same single chip. In such a mixed signal environment, the analogue circuits must use the same process technology as their digital neighbours. With reducing transistor sizes, the impact of process variations on analogue design has become prominent and can lead to circuit performance falling below specification and hence reducing the yield.This thesis explores the methodology and algorithms for an analogue integrated circuit automation tool that optimizes performance and yield. The trade-offs between performance and yield are analysed using a combination of an evolutionary algorithm and Monte Carlo simulation. Through the integration of yield parameter into the optimisation process, the trade off between the performance functions can be better treated that able to produce a higher yield. The results obtained from the performance and variation exploration are modelled behaviourally using a Verilog-A language. The model has been verified with transistor level simulation and a silicon prototype.For a large analogue system, the circuit is commonly broken down into its constituent sub-blocks, a process known as hierarchical design. The use of hierarchical-based design and optimisation simplifies the design task and accelerates the design flow by encouraging design reuse.A new approach for system level yield optimisation using a hierarchical-based design is proposed and developed. The approach combines Multi-Objective Bottom Up (MUBU) modelling technique to model the circuit performance and variation and Top Down Constraint Design (TDCD) technique for the complete system level design. The proposed method has been used to design a 7th order low pass filter and a charge pump phase locked loop system. The results have been verified with transistor level simulations and suggest that an accurate system level performance and yield prediction can be achieved with the proposed methodology

A framework for fine-grain synthesis optimization of operational amplifiers

This thesis presents a cell-level framework for Operational Amplifiers Synthesis (OASYN) coupling both circuit design and layout. For circuit design, the tool applies a corner-driven optimization, accounting for on-chip performance variations. By exploring the process, voltage, and temperature variations space, the tool extracts design worst case solution. The tool undergoes sensitivity analysis along with Pareto-optimality to achieve required specifications. For layout phase, OASYN generates a DRC proved automated layout based on a sized circuit-level description. Morata et al. (1996) introduced an elegant representation of block placement called sequence pair for general floorplans (SP). Like TCG and BSG, but unlike O-tree, B*tree, and CBL, SP is P-admissible. Unlike SP, TCG supports incremental update during operation and keeps the information of the boundary modules as well as their relative positions in the representation. Block placement algorithms that are based on SP use heuristic optimization algorithms, e.g., simulated annealing where generation of large number of sequence pairs are required. Therefore a fast algorithm is needed to generate sequence pairs after each solution perturbation. The thesis presents a new simple and efficient O(n) runtime algorithm for fast realization of incremental update for cost evaluation. The algorithm integrates sequence pair and transitive closure graph advantages into TCG-S* a superior topology update scheme which facilitates the search for optimum desired floorplan. Experiments show that TCG-S* is better than existing works in terms of area utilization and convergence speed. Routing-aware placement is implemented in OASYN, handling symmetry constraints, e.g., interdigitization, common centroid, along with congestion elimination and the enhancement of placement routability

Effective network grid synthesis and optimization for high performance very large scale integration system design

制度:新 ; 文部省報告番号:甲2642号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新480