A bias-correction modeling method of Hammerstein–Wiener systems with polynomial nonlinearities using noisy measurements

Identification for nonlinear systems modelled by deep long short-term memory networks based Wiener model

In this work, we propose a deep reinforcement learning (DRL) framework called Pin-opt, designed to create a reusable solver capable of optimizing pin assignment to minimize signal integrity (SI) and power integrity (PI) degradation in microbump packages. The increasing data rates of high-bandwidth systems have made SI/PI issues critical for ensuring the reliability of these systems. While previous research using meta-heuristic methods has optimized pin assignment to reduce SI/PI degradation in similar vertical interconnections, these approaches tend to be inflexible, providing problem-specific solutions suitable only for square-shaped pin arrangements. Our approach, Pin-opt, leverages the advantages of a learning-based method to create a practical solution applicable to pin maps of any shape and with a very large pin count. By representing pins as graphs during the training process, Pin-opt becomes adaptable to any pin arrangement and demonstrates significant performance improvements when solving large-scale pin assignment problems. We evaluate the performance, computational cost, reusability, and scalability of Pin-opt by comparing it to the genetic algorithm (GA), a conventional meta-heuristic method used for solving optimization tasks. To demonstrate its practicality, Pin-opt is also applied to a pin map of high bandwidth memory (HBM).

Pin-Opt: Graph Representation Learning for Large-Scale Pin Assignment Optimization of Microbumps Considering Signal and Power Integrity

Summary In this article, the parameter learning scheme for the multi‐input multi‐output (MIMO) Hammerstein nonlinear systems under measurement noises is studied, which is derived by exploiting the correlation analysis and data filtering technique. The coupled MIMO Hammerstein system presented involves a static nonlinear subsystem modeled by neural fuzzy model (NFM), and a dynamic linear subsystem established by autoregressive moving average with extra input (ARMAX) model. To learn the unknown parameter of the MIMO Hammerstein system, the combined signals are designed to realize that identification of the nonlinear subsystem is separated from that of linear subsystem. First, the correlation properties of separable signals in a nonlinear system are analyzed, then the parameters of the linear subsystem are estimated utilizing correlation analysis, which can deal with the issue of unmeasured intermediate variable in the Hammerstein system. Second, the data filtering technique is introduced to derive the data filtering‐based recursive least squares technique for learning the nonlinear subsystem parameter, which can reduce the impact of the moving average noise and improve the precision of parameter estimation. Finally, the effectiveness and feasibility of the proposed identification scheme is proved by numerical simulation and nonlinear pH process. 

Parameter learning of multi‐input multi‐output Hammerstein system with measurement noises utilizing combined signals

This paper considers problems related to output error models for output data estimation and parameter identification in missing output data systems. In this regard, a new sequentially parallel distributed adaptive signal processing method with the implementation of a low complexity least squares algorithm is introduced to estimate the parameters of an auxiliary model relative to the original system, as well as handling irregularly missing output data in a stochastic framework. The validation of the proposed distributed architecture using the low complexity least squares algorithm is presented in terms of computational complexity and processing time. Measurement results show that the proposed architecture using the low complexity least squares approach provides fast convergence, parallel linear computational complexity, and significantly reduced processing time compared to the sequentially operated recursive least square (RLS) algorithm for parameter identification in missing output data systems. Finally, the effectiveness of the low complexity least squares algorithm is tested with a system example 

Parameter estimation of the systems with irregularly missing data by using sequentially parallel distributed adaptive signal processing architecture

This article develops a novel separation identification approach for the Hammerstein-Wiener nonlinear systems with process noise using correlation analysis technique. The Hammerstein-Wiener nonlinear systems have three parts, namely, an input nonlinear block, a linear block, and an output nonlinear block. The designed hybrid signals that consist of separable signal and random signal are devoted to achieving parameters separation identification of the Hammerstein-Wiener nonlinear system, that is, the three blocks are identified independently. First, the characteristics of separable signals under the action of static nonlinear block are analyzed, and two groups of separable signals with amplitude relation are utilized to estimate parameters of output nonlinear block. Moreover, the linear block parameters are identified by using correlation analysis approach, which deals with effectively immeasurable problem of internal variable information. Finally, the data filtering technique is implemented to weaken the influence of noises, and filtering-based recursive extended least squares algorithm is developed for figuring out the parameters of nonlinear block and colored noise model. The validity and accuracy of the proposed scheme are verified by two simulations, and simulation results exhibit that the proposed method can obtain higher identification precision and better robustness than the existing identification algorithms. 

Separation identification approach for the
 Hammerstein‐Wiener
 nonlinear systems with process noise using correlation analysis

Correlation analysis-based parameter learning of Hammerstein nonlinear systems with output noise

The crosstalk simulation analysis model complete transmission path is established by HFSS. Based on this model, the near end crosstalk S13 and far end crosstalk S14 of each frequency are obtained. The influence of the maximum radial size of the BGA solder joint, the height of BGA solder joint, the size of the BGA welding disk on the near end crosstalk S13 and the far end crosstalk S14 of the complete transmission path are analyzed. It is found that the crosstalk between two complete transmission paths varies with signal frequency, the maximum radial size of BGA solder joint, solder height and diameter of solder pads.

Crosstalk Simulation Analysis of Complete Transmission Path Based on HFSS

The abnormal delamination of Printed Circuit Board (PCB) during lead-free wave soldering was analyzed by means of visual inspection, cross-section, and thermal analysis. The results showed that PCB absorption moisture causes the saturated vapor pressure during welding inside the substrate exceeds the binding force of the resin and the glass fiber, which induce the delamination of PCB when wave soldering.

Failure Case Analysis for Abnormal Delamination of Printed Circuit Board

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Papers

Separation identification approach for the Hammerstein‐Wiener nonlinear systems with process noise using correlation analysis

Correlation analysis-based parameter learning of Hammerstein nonlinear systems with output noise

Crosstalk Simulation Analysis of Complete Transmission Path Based on HFSS

Failure Case Analysis for Abnormal Delamination of Printed Circuit Board