POWER-EFFICIENT AND HIGH-PERFORMANCE CMOS CIRCUIT DESIGN USING MULTI-LEVEL TRANSISTOR SIZING

Abstract

7. ABSTRACT The present invention provides a method for optimizing CMOS circuits through advanced transistor sizing techniques. The method integrates analytical circuit modelling with a multi-level optimization framework that employs meta-heuristic algorithms, including Genetic Algorithms (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The framework begins with baseline transistor mapping, followed by critical path analysis and iterative optimization to minimize the power-delay product (PDP) while balancing power consumption, delay, and chip area. The optimization process is validated using HSPICE simulations with 0.18µm CMOS technology parameters, achieving significant performance enhancements, including up to a 13% reduction in delay, 5% reduction in power consumption, and 10% increase in circuit speed. The invention’s robustness is confirmed through Monte Carlo simulations with a circuit yield of 98.6%. This systematic methodology enables the design of high-performance and power-efficient CMOS circuits, making it suitable for next-generation VLSI systems and nano scale technology applications. The figure associated with the abstract is Fig. 1.

Specification

Description:4. DESCRIPTION
Technical Field of the invention

The present invention relates to the field of Very Large Scale Integration (VLSI) circuit design, particularly to methods and systems for optimizing the power-delay product (PDP) and other performance parameters in CMOS circuits using multi-level transistor sizing techniques.

Background of the invention

In modern electronics, the ever-increasing demand for faster, smaller, and more energy-efficient devices has driven continuous advancements in Very Large Scale Integration (VLSI) technologies. Complementary Metal-Oxide-Semiconductor (CMOS) technology forms the backbone of integrated circuit (IC) design due to its low power consumption, high scalability, and wide applicability. However, with the rapid scaling of transistor dimensions into the nano scale regime, circuit designers face significant challenges in optimizing critical performance parameters, such as propagation delay, power consumption, and chip area, without compromising functionality.

Transistor sizing, the process of adjusting the channel widths of transistors within a circuit, has long been recognized as a powerful method for improving circuit performance. By appropriately sizing transistors, designers can achieve reduced delay, improved switching speeds, and optimized power dissipation. Despite its effectiveness, transistor sizing presents several challenges due to its non-linear and multi-dimensional nature. A small change in transistor dimensions can have cascading effects on various circuit parameters, creating complex trade-offs that are difficult to resolve using traditional methods.

Historically, approaches to transistor sizing have relied on linear programming, heuristic techniques, or analytical methods. While these methods have achieved moderate success, they often fall short in handling the complexities of modern CMOS circuits, which require simultaneous optimization of multiple conflicting objectives, such as power-delay product (PDP), area, and noise margins. Moreover, traditional methods are computationally intensive and lack flexibility, making them unsuitable for large-scale or highly complex designs.

The need for a systematic, efficient, and scalable approach to transistor sizing has become increasingly evident. This invention addresses these challenges by introducing a multi-level optimization framework that leverages advanced meta-heuristic algorithms. This novel methodology ensures robust optimization, balancing critical trade-offs to achieve superior performance in CMOS circuits, thereby enabling the design of next-generation VLSI systems.

Brief Summary of the invention

The following presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure, and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

It is a primary object of the present invention to provide a transistor sizing method that reduces power consumption and delay in CMOS circuits.

It is yet another object of the present invention to optimize the power-delay product (PDP) and improve overall circuit performance.

It is yet another object of the present invention to provide a systematic methodology that balances trade-offs among power, delay, and chip area.

It is yet another object of the present invention to offer a robust, meta-heuristic-based optimization framework for advanced VLSI designs.

According to an aspect of the present invention, a novel and comprehensive methodology for designing power-efficient and high-performance CMOS circuits by optimizing transistor sizes using a multi-level and multi-objective optimization framework. This invention addresses the pressing challenges faced in modern VLSI design, where the need to balance conflicting parameters such as power consumption, propagation delay, and chip area is paramount, particularly as transistor dimensions continue to scale into the nano scale regime.

In accordance to an aspect of the present invention, the transistor sizing, a process of adjusting the channel widths of transistors, is a well-known method for improving circuit performance. However, it has traditionally been a challenging and computationally intensive task due to the non-linear relationships between circuit parameters. A small change in the size of one transistor can propagate through the circuit, affecting power dissipation, delay, and overall functionality. This invention introduces a systematic approach to resolve these complexities by employing advanced meta-heuristic optimization techniques.

In accordance to an aspect of the present invention, the proposed methodology integrates behavioural circuit equations with optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). These algorithms are chosen for their ability to explore large solution spaces efficiently and balance multiple conflicting objectives. The optimization process begins with modelling the circuit's behaviour using analytical equations. The transistor dimensions are iteratively optimized to minimize the power-delay product (PDP), a critical metric for energy efficiency, while ensuring that other performance parameters remain within design constraints.

In accordance to an aspect of the present invention, the invention includes a design tool implemented in MATLAB. This tool incorporates HSPICE simulations using 0.18µm CMOS technology parameters. The simulation results demonstrate significant improvements in circuit performance. Specifically, the methodology achieves up to a 13% reduction in delay, a 5% reduction in power consumption, and a 10% increase in speed. These results highlight the effectiveness of the proposed approach in optimizing circuit performance while maintaining high accuracy.

In accordance to an aspect of the present invention, the feature of the invention is its adaptability to various circuit designs and applications. The methodology is not limited to specific logic gates or circuit types. It has been successfully applied to full-adder circuits, XOR/XNOR gates, and other components of digital integrated circuits. Furthermore, the optimization framework allows designers to prioritize different performance metrics, providing flexibility to meet specific design goals.

In accordance to an aspect of the present invention, a systematic multi-level approach to optimization. In the first stage, minimum-sized gates are mapped to the circuit to establish a baseline with low power dissipation. In subsequent stages, critical transistors are resized iteratively to meet delay constraints while maintaining minimal power dissipation. This stepwise process ensures that non-critical transistors remain minimally sized, thereby reducing unnecessary power consumption and chip area overheads.

Further objects, features, and advantages of the invention will be readily apparent from the following description of the preferred embodiments thereof, taken in conjunction with the accompanying drawings.

Brief Description of the Drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

Fig. 1 illustrates the input and output behaviour of CMOS circuits under varying loads, in accordance with an exemplary embodiment of the present invention;

Fig. 2 illustrates the layout design of a CMOS circuit, highlighting the critical paths and the arrangement of transistors, in accordance with an exemplary embodiment of the present invention;

Fig. 3 illustrates the XOR/XNOR circuit configuration within a full-adder cell, in accordance with an exemplary embodiment of the present invention;

Fig. 4 illustrates the a comparative analysis of circuit performance metrics between existing and proposed transistor sizing methodologies, in accordance with an exemplary embodiment of the present invention;

Fig. 5 illustrates the input stimulus applied to a full-adder circuit and its resulting outputs, Sum and Carry (Cout), in accordance with an exemplary embodiment of the present invention;

Detailed Description of the invention

It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. In addition, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

The use of "including", "comprising" or "having" and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Further, the use of terms "first", "second", and "third", and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

According to an exemplary embodiment of the present invention, a novel approach to optimizing CMOS circuits by employing advanced transistor sizing techniques. As transistor dimensions scale into the nano scale regime, the design of efficient and high-performance circuits has become increasingly challenging. This invention addresses the inherent complexities of transistor sizing, including the need to balance power consumption, propagation delay, and chip area, while ensuring robust circuit functionality. By combining analytical modelling and meta-heuristic optimization algorithms, the invention offers a systematic and efficient methodology for achieving superior circuit performance.

In accordance with an exemplary embodiment of the present invention, at the core of the invention is the concept of transistor sizing, which involves adjusting the channel widths of transistors to optimize circuit behaviour. Transistor dimensions directly impact parameters such as resistance, capacitance, and switching speed. Increasing the width of a transistor reduces resistance and delay but increases capacitance and power dissipation. The challenge lies in determining the optimal size for each transistor in a circuit to balance these conflicting parameters. Traditional sizing methods, including linear programming and heuristic approaches, often struggle with the non-linear relationships and multi-dimensional trade-offs involved in modern CMOS circuits. This invention overcomes these limitations by framing transistor sizing as a multi-objective optimization problem.

In accordance with an exemplary embodiment of the present invention, an analytical modelling phase, where the circuit's behavior is described using mathematical equations. These equations capture the relationships between transistor dimensions and performance metrics, including power consumption, delay, and parasitic capacitances. Delay is modelled as a function of resistance and capacitance, where resistance is inversely proportional to transistor width, and capacitance increases with size. Power consumption is modelled by incorporating both dynamic and static components, taking into account switching activity and leakage currents. These analytical models form the basis for defining the fitness functions used in the optimization process.

In accordance with an exemplary embodiment of the present invention, the feature of the invention is its multi-level optimization framework, which is designed to systematically enhance circuit performance. In the first stage, the circuit is mapped with minimum-sized transistors to establish a baseline configuration with low power dissipation. The second stage involves analysing the circuit's critical paths, which are the most significant contributors to delay. By focusing on these paths, the optimization process achieves maximum delay reduction without unnecessary modifications to non-critical transistors. The third stage employs iterative optimization using advanced meta-heuristic algorithms. These include Genetic Algorithms (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO), each offering unique advantages in exploring large solution spaces and balancing multiple objectives.

In accordance with an exemplary embodiment of the present invention, genetic Algorithms mimic the process of natural selection, evolving transistor dimensions over successive generations to optimize performance. Simulated Annealing replicates the annealing process in metallurgy, gradually refining solutions while avoiding local optima. Particle Swarm Optimization leverages principles of collective intelligence, adjusting transistor sizes based on collaborative decision-making. These algorithms work in conjunction to minimize the power-delay product (PDP) while maintaining other performance constraints.

In accordance with an exemplary embodiment of the present invention, it includes a design tool implemented in MATLAB, which integrates the analytical models and optimization algorithms. This tool interfaces with HSPICE simulations to validate the optimized designs. The methodology has been applied to circuits designed with 0.18µm CMOS technology parameters, demonstrating its efficacy in real-world scenarios. Performance results highlight significant improvements, including up to a 13% reduction in delay, a 5% reduction in power consumption, and a 10% increase in circuit speed. The optimized transistor sizes also result in lower parasitic effects, enhancing the overall reliability and efficiency of the circuits.

In accordance with an exemplary embodiment of the present invention, Monte Carlo simulations further validate the robustness of the methodology. Using 30,000 statistical samples, the invention achieves a circuit yield of approximately 98.6%, confirming its reliability under process variations and manufacturing uncertainties. The multi-level approach ensures that critical transistors are resized effectively, while non-critical transistors remain minimally sized, reducing unnecessary power consumption and chip area overheads.

In accordance with an exemplary embodiment of the present invention, it is adaptable to various CMOS circuit designs, including full-adder circuits, XOR/XNOR gates, and other digital integrated circuits. Its flexibility allows designers to prioritize specific performance metrics based on application requirements. For instance, battery-operated devices can benefit from minimized power consumption, while high-performance computing systems can leverage enhanced speed. The systematic methodology also ensures scalability, making it suitable for complex circuits with thousands of transistors.

Referring to Figs, Figure1 illustrates the input and output behaviour of CMOS circuits under varying loads. The figure depicts how input load and output capacitance influence the propagation delay and switching activity of a CMOS gate. By modelling these dependencies, the figure provides foundational insights into the optimization of delay and power consumption through transistor sizing.

Fig. 2 presents the layout design of a CMOS circuit, highlighting the critical paths and the arrangement of transistors. It visually represents how transistor dimensions impact circuit performance, including resistances, capacitances, and the associated power-delay trade-offs. The figure emphasizes the role of transistor placement and channel width adjustments in the optimization process.

Fig. 3 shows the XOR/XNOR circuit configuration within a full-adder cell. It highlights the optimization parameters, including channel width coefficients (Ki), used for balancing power-delay product (PDP) in the circuit. The visual aids in understanding the design's multi-dimensional optimization space and how changes in transistor sizes influence overall performance.

Fig. 4 provides a comparative analysis of circuit performance metrics between existing and proposed transistor sizing methodologies. The table compares delay, power consumption, and speed, showing significant improvements achieved by the proposed method. Graphical elements may be included to illustrate these performance enhancements clearly.

Fig. 5 shows the input stimulus applied to a full-adder circuit and its resulting outputs, Sum and Carry (Cout). It includes a series of 56 test patterns representing all possible transitions, showcasing how the circuit handles different input combinations. The figure illustrates the relationship between input switching activity and internal power dissipation within the circuit. , Claims:. CLAIMS
I/We Claim:
1. A method for optimizing CMOS circuits by transistor sizing, the method comprising:
a. modelling circuit behaviour using analytical equations to define relationships between transistor dimensions and circuit performance parameters, including power consumption, propagation delay, and parasitic capacitances;
b. employing a multi-level optimization framework that iteratively adjusts transistor channel widths to achieve an optimal balance among power-delay product (PDP), power consumption, and delay constraints, wherein the multi-level optimization framework includes:
i. baseline mapping of the circuit with minimum-sized transistors to establish an initial configuration,
ii. critical path analysis to identify and prioritize high-impact transistors for optimization, and
iii. iterative optimization using meta-heuristic algorithms selected from Genetic Algorithms (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO);
c. validating the optimized transistor sizes through circuit simulations, wherein the simulations ensure compliance with performance constraints and process variation tolerances; and
d. implementing the optimized transistor dimensions in CMOS circuit designs to enhance performance by reducing power consumption, propagation delay, and internal parasitic effects.

2. The method as claimed in claim 1, wherein the analytical equations used for modelling circuit behaviour are derived to explicitly calculate power consumption as a combination of dynamic and static power components, incorporating factors such as switching activity, capacitance, and leakage currents.

3. The method as claimed in claim 1, wherein the delay model is represented as a function of resistance and capacitance (RC), where resistance is inversely proportional to transistor width, and capacitance is directly proportional to the transistor dimensions.

4. The method as claimed in claim 1, wherein the multi-level optimization framework prioritizes transistor resizing based on critical path analysis, wherein critical paths are identified as the circuit paths with the highest cumulative propagation delay.

5. The method as claimed in claim 1, wherein the Genetic Algorithm (GA) employed in the iterative optimization step uses operations including selection, crossover, and mutation to evolve transistor sizes over successive generations.

6. The method as claimed in claim 1, wherein the Simulated Annealing (SA) algorithm employed in the iterative optimization step simulates an annealing process by gradually reducing the search space to refine transistor sizes while avoiding local optima.

7. The method as claimed in claim 1, wherein the Particle Swarm Optimization (PSO) algorithm employed in the iterative optimization step adjusts transistor dimensions based on collective intelligence principles, wherein each transistor size is influenced by the best solution found by the swarm.

8. The method as claimed in claim 1, wherein the validation step is performed using HSPICE simulations on CMOS circuits designed with 0.18µm technology parameters, ensuring the optimized transistor sizes achieve specified performance targets.

9. The method as claimed in claim 1, wherein the implementation of the optimized transistor sizes reduces power consumption by at least 5%, propagation delay by at least 13%, and improves speed by at least 10% compared to the baseline configuration.

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