Automated Analog Circuit Design Research Articles

A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuit

When applying evolutionary algorithms to circuit design automation, circuit representation is the first consideration. There have been several studies applying different circuit representations. However, they still have some problems, such as lack of design ability, which means the diversity of evolved circuits was limited by the circuit representation, and inefficient transformation from circuit representation into SPICE (Simulation Program with Integrated Circuit Emphasis) netlist. In this paper, a novel tree-based circuit representation for analog circuits is proposed, which is equipped with an intuitive and three-terminal devices friendly mapping rule between circuit representation and SPICE netlist, as well as a suitable crossover operator. Based on the proposed representation, a framework for automated analog circuit design using genetic programming is proposed to evolve both the circuit topology and device values. Three benchmark circuits are applied to evaluate the proposed approach, showing that the proposed method is feasible and evolves analog circuits with better fitness and number of components while using less fitness evaluations than existing approaches. Furthermore, considering physical scalability limits of conventional circuit elements and the increased interest in emerging technologies, a memristor-based pulse generation circuit is also evolved based on the proposed method. The feasibility of the evolved circuits is verified by circuit simulation successfully. The experiment results show that the evolved memristive circuit is more compact and has better energy efficiency compared with existing manually-designed circuits.

Smart-MSP: A Self-Adaptive Multiple Starting Point Optimization Approach for Analog Circuit Synthesis

Automated analog circuit design is promising for increasing the design productivity and narrowing the time-to-market, but is facing the bottleneck of tremendous design complexity. In this paper, a simulation-based optimization approach named smart-multiple starting point (MSP) is proposed for analog circuit synthesis. The proposed smart-MSP is based on the framework of MSP optimization, which is shown to be much more efficient than other global optimization methods like simulated annealing, genetic algorithm, particle swarm optimization, etc. Efficient techniques including heuristic-biased starting point selection, sparse regression and probabilistic TABU are developed in smart-MSP and make the algorithm quite smart in a way that the overall optimization process is self-adaptive by learning from the previous local searches and can efficiently produce optimal results to approximate the global optimum. Experiments have demonstrated that the proposed smart-MSP is ${2.6-12.5\times }$ faster than the original MSP method, and is $ {1.3-2100\times }$ faster than other state-of-the-art methods.

Automated Analog Circuit Design Synthesis Using A Hybrid Genetic Algorithm with Hyper-Mutation and Elitist Strategies

Analog circuits are of great importance in electronic system design. Analog circuit design consists of circuit topology design and component values design. These two aspects are both essential to computer aided analog circuit evolving. However, Traditional GA is not very efficient in evolving circuit component's values. This paper proposed a hybrid algorithm HME-GA (GA with hyper- mutation and elitist strategies). The advantage of HME-GA is that, it not only concentrates on evolving circuit topology, but also pays attention to evolving circuit component's values. Experimental results show that, the proposed algorithm performs much better than traditional GA. HME- GA is an efficient tool for analog circuit design. Evolutionary technology has been demonstrated to be very useful in computer aided analog circuit design. More potential of evolutionary methods on analog circuit design is waiting for exploring. Index Terms—Hyper mutation, elitist, GA, analog circuit design

Automated analog circuit design using two-layer genetic programming

Analog circuits are very important in many high-speed applications such as communications. Since the size of analog circuit is becoming larger and more complex, the design is becoming more and more difficult. This paper proposes a two-layer evolutionary scheme based on genetic programming (GP), which uses a divide-and-conquer approach to evolve the analog circuits. Corresponding to the two-layer GP, a new representation of circuit has been proposed here and it is more helpful to generate expectant circuit graphs. This algorithm can evolve the circuits with dynamical size, circuit topology, and component values. The experimental results on the designs of the voltage amplifier and the low-pass filter show that this algorithm is efficient.