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چکیده
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Electronic circuit design is of great importance because these circuits are the basis for the operation of all electronic devices. Without optimal circuit design, electronic devices cannot deliver their expected performance. Optimization in circuit design can lead to reduced energy consumption, increased durability and stability, and reduced production costs [1]. Also, accurate and efficient circuit design ensures that devices can work well in various environments and operating conditions. In industries such as medicine, automotive, and telecommunications, where the correct and safe operation of equipment is critical, the quality of circuit design directly affects safety and efficiency. In addition, proper circuit design can lead to innovation in new technologies and the development of more advanced products, which strengthens the competitive position of companies in global markets [2]. From smart home devices to advanced space systems, all are built on the basis of high-performance electronic circuits. As these circuits increase in complexity, traditional design methods face many challenges. Meanwhile, machine learning and neural networks, as advanced artificial intelligence technologies, have revolutionized circuit design. These methods, with their ability to analyze large amounts of data, simulate complex scenarios, and provide optimal solutions, help engineers achieve better results in less time. For example, neural networks can accurately model the behavior of nonlinear circuits, and reinforcement learning algorithms can be used to design circuits that consume more energy. These advances have enabled the creation of smaller, faster, and more efficient devices that meet the growing needs of advanced industries [3]. Despite these advancements, integrating AI methodologies into electronic circuit design is not without challenges. Achieving an optimal balance between performance, resource utilization, and design complexity remains a critical issue. Furthermore, whi
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