Progress in neural network based techniques for signal integrity analysis–a survey

With the increase in data rates, signal integrity analysis has become more time and memory intensive. Simulation tools such as 3D electromagnetic field solvers can be accurate but slow, whereas faster models such as design equations and equivalent circuit models lack accuracy. Artificial neural networks (ANNs) have recently gained popularity in the RF and microwave circuit modeling community as a new modeling tool. This has in turn spurred progress towards applications of neural networks in signal integrity. A neural network can learn from a set of data generated during the design process. It can then be used as a fast and accurate modeling tool to replace conventional approaches. This paper reviews the recent advancement of neural networks in the area of signal integrity modeling. Key advancements are considered, particularly those that assist the ability of the neural network to cope with an increasing number of inputs and handle large amounts of data

Power-Ground Plane Impedance Modeling Using Deep Neural Networks and an Adaptive Sampling Process

This paper proposes a deep neural network (DNN) based method for the purpose of power-ground plane impedance modeling. A composite DNN model, which is a combination of two DNNs is used to predict the Z-parameters of power ground planes from their design parameters. The first DNN predicts the normalized Z-parameters whereas the second DNN predicts the original maximum and minimum values of the non-normalized Z-parameters. This allows the method to retain a high accuracy when predicting responses that have large variations across designs, as is the case with the Z-parameters of the power-ground planes. We use the adaptive sampling algorithm to generate the training and validation samples for the DNNs. The adaptive sampling algorithm starts with only a few samples, then slowly generates more samples in the non-linear regions within the design parameters space. The level of non-linearity of the regions is determined by a surrogate model which is also trained using the generated samples as well. If the surrogate model has poor prediction accuracy in a region, then the adaptive sampling algorithm will generate more samples in that region. A shallow neural network is used as the surrogate model for non-linearity determination of the regions since it is faster to train and update. Once all the samples have been generated, they will be used to train and validate the composite DNN models. Finally, we present two examples, a square-shaped power ground plane and a square-shaped power ground plane with a hollow square at the center to demonstrate the robustness of the DNN composite models

EYE-HEIGHT/WIDTH PREDICTION USING ARTIFICIAL NEURAL NETWORKS FROM S-PARAMETERS WITH VECTOR FITTING

Artificial neural networks (ANNs) have been used to model microwave and RF devices over the years. Conventionally, S-parameters of microwave/RF designs are used as the inputs of neural network models to predict the electrical properties of the designs. However, using the S-parameters directly as inputs into the ANN results in a large number of inputs which slows down the training and configuration process. In this paper, a new method is proposed to first disassemble the S-parameters into poles and residues using vector fitting, and then the poles and residues are used as the input data during configuration and training of the neural networks. Test cases show that the ANN trained using the proposed method is able to predict the eye-heights and eye-widths of typical interconnect structures with minimal error, while showing significant speed improvement over the conventional method

Eye Diagram Modeling Of High-Speed Channels Using Artificial Neural Networks With An Improved Adaptive Sampling Algorithm

xxiii EYE DIAGRAM MODELING OF HIGH-SPEED CHANNELS USING ARTIFICIAL NEURAL NETWORKS WITH AN IMPROVED ADAPTIVE SAMPLING ALGORITHM ABSTRACT As data rates increase to the gigabit range and beyond, signal integrity (SI) analysis becomes increasingly difficult and time consuming process. Thus, many researchers have started to look out for artificial neural networks (ANNs) as an alternative to traditional SI modeling tool because ANNs are easy to use and fast. However, large amount of samples need to be generated for the training process of the ANN for the modeling of a complex design, resulting in a high neural model development cost. The adaptive sampling technique is used for the data generation due to its flexibility where it generates samples according to the non-linearity of the regions in the design space. This work proposes an improvement to the original adaptive sampling algorithm and uses it as the sampling method for eye diagram modeling. This reduces the number of training samples by 16.1%, validation samples by 14.7% and neural model development time by 23%. Besides that, the use of the prior knowledge input neural network (PKI-ANN) and the prior knowledge input difference neural network (PKID-ANN) for the modeling of high dimensional SI problem is proposed. The normalized worst-case error for the PKI-ANN is only 6.66% and for the PKID-ANN is only 6.32% as compared to that of the conventional ANN which is 11.44%. Finally, the neural network technique for the modeling of entire bit rate error (BER) contours is proposed which provides engineers with more information, such as the full shape of eye instead rather than just the height and width of the eye. An Average testing performance of R2=0.983 is achieved for the BER contour neural modeling technique

Temporal convolutional networks for transient simulation of high-speed channels

While the recurrent neural network (RNN) architecture has been the go-to model in transient modeling, recently the temporal convolutional network (TCN) has been garnering more attention as it has a longer memory than recurrent architectures with the same capacity. In this paper, we propose the use of the TCN for transient simulation of high-speed channels. The adaptive successive halving algorithm (ASH-HPO) is used to perform automated hyperparameter optimization for the TCN. It has two components, progressive sampling and successive halving. It iteratively expand the size of training dataset and eliminates a certain percentage of bad performing models. The progressive sampling component is modified to preserve the original sequencing of time series data to prevent information leakage. Also, the successive halving component is modified so that each eliminated model must be validated using at least two different validation datasets before it is being removed. The robustness of the proposed method is demonstrated using four high-speed channel examples, and the TCN is compared against existing convolutional neural network long short-term memory (CNN-LSTM) and dilated causal convolution (DCC) models. The TCN outperforms the other models consistently in all four tasks in terms of training speed, amount of training data to converge, and accuracy