A nonlinear electromagnetic inversion scheme incorporating a deep-learning-based plug-andplay (PNP) regularization approach is proposed. Unlike conventional regularization techniques that rely on fixed priors, the PNP method decouples the data fidelity and regularization terms, enabling the integration of a learned prior via a state-of-the-art denoiser. The recently introduced Swin-Conv-UNet (SCUNet), known for its superior image denoising capabilities by leveraging swin transformer blocks and residual convolutional blocks in its architecture, is employed as the PNP denoiser within a multi-frequency Gauss-Newton nonlinear inversion framework. The resulting PNP-SCUNet inversion scheme is evaluated on both synthetic and experimental data, demonstrating superior performance compared to traditional inversion methods, including Tikhonov and total variation, and previously developed PNP regularization with block-matching and 3-D filtering (BM3D) denoiser.