Abstract In this paper, we first establish a model of ultra-large-scale integrated circuits and study the model architecture from basic circuit units to complex circuit units. Then, the circuit optimization problem is mathematically analyzed, and the unconstrained and constrained parametric optimization problems with electrical parameters are investigated. Reinforcement learning is introduced to a reasonably one-to-one correspondence between the parametric optimization problem and the environment in reinforcement learning, which transforms the ordinary optimization problem into a task of reinforcement learning and realizes the optimization of electrical parameters in integrated circuit design. Finally, the effect of optimizing the electrical parameters of the method in this paper is evaluated. In the case of 200 DPPM, 300 DPPM, and 400 DPPM, the number of censored test parameters of this paper’s method is distributed in the range of (10,15), while the number of censored test parameters of the other methods are in the interval of (2,10), and this paper’s method outperforms the other methods. This study has an important reference value to improve the efficiency, reliability, and performance of integrated circuit design, and can provide a reference for the design of integrated circuits. 

Methodology for optimization of electrical parameters in the design of very large-scale integrated circuits

Metaheuristics for analog circuit design optimization: A survey

High-precision Vernier-type Optoelectronic Integrated Chip Design

We propose a machine learning-driven optimisation framework for analog circuit design in this paper. Machine learning based global offline surrogate models, with the circuit design parameters as the input, are built in the design space for the analog circuits under study and are used to guide the optimisation algorithm towards an optimal circuit design, resulting in faster convergence and reduced number of spice simulations. Multi-layer perceptron and random forest regressors are employed to predict the required design specifications of the analog circuit. Multi-layer perceptron classifiers are used to predict the saturation condition of each transistor in the circuit. We validate the proposed framework using three circuit topologies—a bandgap reference, a folded cascode operational amplifier, and a two-stage operational amplifier. The simulation results show better optimum values and lower standard deviations for fitness functions after convergence, with a reduction in spice calls by 56%, 59%, and 83% when compared with standard approaches in the three test cases considered in the study.

Machine Learning Driven Global Optimisation Framework for Analog Circuit Design

This study explores the optimization of solar energy capture through the implementation of a dual-axis solar tracking system, coupled with advanced simulation using the PVsyst software. The objective is to enhance energy efficiency by precisely aligning photovoltaic panels with the sun's path. Our research investigates the performance of this system under varying environmental conditions and geographical locations. The PVsyst simulation offers valuable insights into energy generation potential, efficiency gains, and financial viability. Preliminary results indicate a substantial increase in energy yield compared to fixed panels, especially in regions with dynamic solar angles. The findings from this study underscore the significance of simulation-based approaches in assessing and maximizing the effectiveness of dual-axis.

Optimizing Solar Energy Harvesting: Dual-Axis Solar Tracking and Simulation Analysis

An automated sizing approach for analog circuits using evolutionary algorithms is presented in this paper. A targeted search of the search space has been implemented using a particle generation function and a repair-bounds function that has resulted in faster convergence to the optimal solution. The algorithms are tuned and modified to converge to a better optimal solution with less standard deviation for multiple runs compared to standard versions. Modified versions of the artificial bee colony optimisation algorithm, genetic algorithm, grey wolf optimisation algorithm, and particle swarm optimisation algorithm are tested and compared for the optimal sizing of two operational amplifier topologies. An extensive performance evaluation of all the modified algorithms showed that the modifications have resulted in consistent performance with improved convergence for all the algorithms. The implementation of parallel computation in the algorithms has reduced run time. Among the considered algorithms, the modified artificial bee colony optimisation algorithm gave the most optimal solution with consistent results across multiple runs. 

pdf/performance-evaluation-of-evolutionary-algorithms-for-analog-5bync3y8s4.pdf

Performance evaluation of evolutionary algorithms for analog integrated circuit design optimisation

The angle of incidence of light significantly influences the energy harvested by a solar panel. A dual-axis altazimuth mount enables the solar panel to track the Sun when it is in the panel’s field of view to make light fall normal to the panel’s face. An adaptive algorithm based sensor-less dual-axis solar tracking system is proposed which enables the panel to track the brightest object in its range using the power output as a guide which helps to track the true source as it eliminates the mismatches between an additional sensor and the actual panel output. A frugal prototype of the proposed system is implemented and used to collect data that indicates the advantages of the solar tracking system. The average energy harvested per day by the system is found to be 75Wh in tracking mode and 56Wh in static mode of operation that shows a 34% improvement in the yield of the solar panel.

Adaptive Algorithm Based Sensor-less Dual-Axis Solar Tracking System

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Performance evaluation of evolutionary algorithms for analog integrated circuit design optimisation

Adaptive Algorithm Based Sensor-less Dual-Axis Solar Tracking System