The inherent complexities and limitations of manual analog circuit sizing have driven researchers to develop a variety of automated methodologies. These approaches generally fall into two main categories: knowledge-driven techniques, which encode expert insights and design heuristics , and optimization-based strategies, which leverage computational algorithms to explore optimal solutions. 

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One of the early approaches was the knowledge-based approach, where expert designers transform their heuristic design procedure (plan), which is based on knowledge and experience, into a computer program. One main drawback of this approach is the large discrepancies between the results of the authored design plan and the simulation results, because the designer uses simplified models in the plan.

First, in the knowledge based approaches, circuit designers incorporate circuit equations into computer programs. Directly embedded manual circuit design processes in a Python-code framework. The direct codification of the manual process enabled analog circuit designers to ease the creation of analog generators. However, the knowledge-based approaches generally require a closed form solution for a given target. To meet the given target using prior knowledge, an inaccuracy occurs by oversimplifying the given structure.

In the knowledge based design method, the expertise and knowledge of experts are integrated into the design plan, forming a systematic design process. The shaping of this process starts during the initial design strategy phase, to ensure that the final design meets the established performance standards via design equations and strategic approaches. The designer solves all the equations derived in the process of design planning based on professional knowledge. After selecting an appropriate topology, following this plan usually can obtain an initial design solution, but it is very time consuming.

In the knowledge-based techniques, designers transcribe their domain experience into equations and algorithms, which may become increasingly difficult to generate even approximate specifications from simple design equations as technology advances. 

Comprehension of circuitry is effectively converted into procedural techniques and mathematical formulations in the knowledge-based methods. Nevertheless, the scalability of these approaches is limited since they rely on the circuit designers’ expertise.

Knowledge-based techniques are designer-centric. They customize the design steps for specific circuits based on domain knowledge and embed them into procedural scripts that mimic the actions of designers. These scripts allow designers to have full control over the modification and debugging of circuits to guarantee design reliability. However, design efficiency is significantly thwarted, because designers, acting as optimization agents, are required to frequently interact with procedural scripts.