Document Type : Original Research Paper

Authors

• Atousa Gholami Boorkheyli ¹
• Majid Babaeinik ¹
• Hadi Dehbovid ²
• Vahid Ghods ¹

¹ Department of Electrical Engineering, Se.C., Islamic Azad University, Semnan, Iran.

² Department of Electrical Engineering, No.C., Islamic Azad University, Noor, Iran.

Abstract

Background and Objectives: This research aims to optimize component placement in integrated systems using evolutionary algorithms. The primary goal is to generate a compact floorplan while satisfying design constraints, particularly in analog circuits where symmetry and proximity constraints are critical to minimizing coupling interference and enhancing performance. The study proposes using a convolutional neural network (CNN) to extract these placement constraints, with its parameters optimized via the non-dominated sorting genetic algorithm III (NSGA-III). Additionally, a hybrid routing approach combining simulated annealing (SA) and NSGA-III is introduced to improve routing efficiency through multi-objective optimization.
Methods: The placement constraints, including symmetry and proximity requirements, are extracted using a CNN, whose parameters are optimized by NSGA-III. For routing, a hybrid approach is employed where SA generates initial routing solutions, which are then refined by NSGA-III for multi-objective optimization. The proposed method is implemented on a two-stage recycling folded cascade (RFC) amplifier in 0.18μm CMOS technology with a 1.8V supply voltage. A dedicated MATLAB toolbox is developed to facilitate placement while adhering to design rules using optimization algorithms.
Results: Simulation results confirm the effectiveness of the proposed methodology, demonstrating optimized placement and routing with improved circuit performance. The combination of CNN and NSGA-III successfully generates a compact and efficient layout, while the hybrid routing approach (SA + NSGA-III) enhances the routing process. The RFC amplifier case study shows better utilization of physical resources and performance improvements, validating the method's efficiency.
Conclusion: This study demonstrates that the proposed method, integrating evolutionary algorithms and CNN, effectively optimizes placement and routing in integrated systems. The CNN-based constraint extraction and NSGA-III optimization enable compact layouts, while the hybrid routing approach improves multi-objective optimization. Simulations on the RFC amplifier confirm enhanced circuit performance and resource utilization. This method offers significant advantages over traditional approaches and is applicable to complex and industrial designs.

Keywords

• Analog Circuit Placement
• Evolutionary Algorithms
• Convolutional Neural Network
• NSGA-III, Simulated Annealing

Main Subjects

• Analogue Integrated Circuits