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The experimental results demonstrate that, compared to the average score of the 10 commercial LLMs listed in our experiments, our method shows a functional correctness increase of 183.41% on the NetlistEval dataset and a 91.07% increase on NGen. 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On the other hand, little research has been done for logic synthesis on the netlist generation. A direct application of LLMs for netlist generation presents additional challenges due to the scarcity of netlist-specific data, the need for tailored fine-tuning, and effective generation methods. This work first presents a novel training set and two evaluation sets catered for direct netlist generation LLMs, and an effective dataset construction pipeline to construct these datasets. Then this work proposes LLM4Netlist, a novel step-based netlist generation framework via fine-tuned LLM. The framework consists of a step-based prompt construction module, a fine-tuned LLM, a code confidence estimator, and a feedback loop module, and is able to generate netlist codes directly from natural language functional descriptions. We evaluate the efficacy of our approach with our novel evaluation datasets. 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