This chapter delves into the practical implementation of the proposed architectures and the corresponding datasets. It begins by examining both datasets, focusing on its integration with various feature engineering techniques and the model training process. The chapter proceeds with a discussion on the implementation and optimization of the supervised learning approaches, serving as a benchmark for the different configurations of the proposed model. Next, it details the implementation of the auxiliary network, followed by the development of the Denoising Diffusion Probabilistic Models (DDPM), which forms the foundation of the approach for the Voltage Combiners biased Operational Transconductance Amplifier (VCOTA) circuit topology. Building on this, the denoising Artificial Neural Network (ANN) architectures are implemented. After discussing the implementation and optimization of the model for the first dataset, the same process and an analysis was conducted for the Folded VCOTA dataset. All models were implemented using the PyTorch library [1] and trained on a personal laptop equipped with an NVIDIA GeForce RTX 3070 Ti GPU@.