Introduction to Electromagnetic Compatibility

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

Artificial Neural Networks Based Optimization Techniques: A Review

This paper presents an effective loop impedance extraction method and a model of a signal through-silicon via (TSV) surrounded by multiground TSVs. According to this method, the effective coupling substrate capacitances of multiground TSVs with different numbers and placements are calculated. Based on the calculated values of the resistance-inductance-capacitance-conductance (RLCG) parameters, the equivalent circuit and a two-port network model are established. The S -parameters of the model are validated by the simulated and measured results. Then, the effect of different patterns of ground TSVs on the central signal and coupling capacitance are discussed. Note that the hexagon pattern proposed in this paper can save the occupied area prominently without damaging the signal integrity.

Modeling and Optimization of Multiground TSVs for Signals Shield in 3-D ICs

The current demand in Power Distribution Network (PDN) design is characterized by the accurate placement of decoupling capacitors and the minimization of their number aimed at cost saving. The paper proposes an optimization algorithm for accordingly placing decoupling capacitors one-by-one and iteratively evaluating the cost function of each PDN design solution. This allows the designer to identify the minimum number of decaps whenever the input impedance satisfies the target impedance requirements. The algorithm is based on the Genetic Algorithm accordingly adapted for the specific application of PDN design. It may involve the evaluation of the input impedance at multiple locations, representing either multiple ICs, as well as multiple power input areas/pins of the same IC. The validation of the developed optimization algorithm is carried out by applying it to a manufactured PCB and by employing typical (low inductance) decaps for PDN design. The optimization process led to a decap configuration that effectively takes into account the decap value, the parasitics inductance, and the decap location. An accurate experimental test further validates the optimized PDN.

Genetic Algorithm PDN Optimization based on Minimum Number of Decoupling Capacitors Applied to Arbitrary Target Impedance

In this paper, we measured and analyzed glass interposer power distribution network (PDN) resonance effects on a high-speed through glass via (TGV) channel for the first time. To verify the glass interposer PDN resonance effects on the TGV channel, glass interposer test vehicles were fabricated. With these test vehicles, glass interposer PDN impedance, channel loss, far-end crosstalk, and eye diagram are measured. Based on these measurements, glass interposer PDN resonance effects on the signal integrity of the high-speed TGV channel are analyzed. Due to low loss of the glass substrate, sharp high PDN impedance peaks are generated at the resonance frequencies. High PDN impedance peaks at the PDN resonance frequencies, which affect return current of the TGV channel, increase channel loss, crosstalks, and PDN noise coupling in the frequency domain and degrade eye diagram in the time domain. To suppress these glass interposer PDN resonance effects, a ground shielded-TGV scheme is proposed. The proposed ground shielded-TGV scheme includes two ground TGVs $200\;{{\mu \text{m}}}$ away from the signal TGV considering the design rules and includes package ground underneath the glass interposer. Effectiveness of the suggested grounding scheme on the resonance effects suppression is verified with three-dimensional electromagnetic simulation. The proposed shielded-TGV design successfully suppressed the glass interposer PDN resonance effects that results in the suppression of insertion loss, shielding of the crosstalk, and improvement of the eye diagram of the high-speed TGV channel.

Measurement and Analysis of Glass Interposer Power Distribution Network Resonance Effects on a High-Speed Through Glass Via Channel

An iterative optimization for decoupling capacitor placement on a power delivery network (PDN) is presented based on Genetic Algorithm (GA) and Artificial Neural Network (ANN). The ANN is first trained by an appropriate set of results obtained by a commercial simulator. Once the ANN is ready, it is used within an iterative GA process to place a minimum number of decoupling capacitors for minimizing the differences between the input impedance at one or more location, and the required target impedance. The combined GA–ANN process is shown to effectively provide results consistent with those obtained by a longer optimization based on commercial simulators. With the new approach the accuracy of the results remains at the same level, but the computational time is reduced by at least 30 times. Two test cases have been considered for validating the proposed approach, with the second one also being compared by experimental measurements.

https://www.mdpi.com/2079-9292/9/8/1243/pdf

Effective PCB Decoupling Optimization by Combining an Iterative Genetic Algorithm and Machine Learning

The high level of integration in digital electronic chips based on three-dimensional (3D) technology requires accurate modelling of the vertical interconnects (the through silicon vias – TSVs). An accurate prediction of the signal propagation and crosstalk of the TSVs cannot be based on the single via since the interaction among adjacent TSVs in a high density array cannot be neglected. An algorithm is proposed that extends the approach for modelling a TSV array with a complete analytical evaluation of the final multiport scattering parameter matrix, thus making the electromagnetic modelling of such structures more efficient without relying on commercial circuit solvers. The proposed method requires much less processing time with respect to commercial solvers with a comparable accuracy.

Analytical evaluation of scattering parameters for equivalent circuit of through silicon via array

Decoupling capacitors are fundamental keys for the reduction of transient noise in power delivery networks; their arrangement and values are crucial for reaching this goal. This work deals with the optimization of the decoupling capacitors of a power delivery network by using a nature-inspired algorithm. In particular, the capacitance value and the location of three decoupling capacitors are optimized in order to obtain an input impedance below a specific mask, by using a nature-inspired algorithm, the genetic one, in combination with two electromagnetic solvers used to compute the objective function. An experimental board is designed and manufactured; measurements are performed to validate the numerical results.

https://www.mdpi.com/2079-9292/8/7/737/pdf

Decoupling Capacitors Placement at Board Level Adopting a Nature-Inspired Algorithm

To reduce the noise created by a power delivery network, the number, the value of decoupling capacitors and their arrangement on the board are critical to reaching this goal. This work deals with specific improvements, implemented on a genetic algorithm, which used for the optimization of the decoupling capacitors in order to obtain the frequency spectrum of the input impedance in different positions on the network, below previously defined values. Measurements are performed on a specifically manufactured board in order to validate the effectiveness of the proposed algorithm and the optimization results obtained for a specific example board.

/pdf/efficient-iterative-process-based-on-an-improved-genetic-ayr4ntpvgu.pdf

Riccardo Cecchetti

Papers

Genetic Algorithm PDN Optimization based on Minimum Number of Decoupling Capacitors Applied to Arbitrary Target Impedance

Effective PCB Decoupling Optimization by Combining an Iterative Genetic Algorithm and Machine Learning

Analytical evaluation of scattering parameters for equivalent circuit of through silicon via array

Decoupling Capacitors Placement at Board Level Adopting a Nature-Inspired Algorithm

Efficient Iterative Process Based on an Improved Genetic Algorithm for Decoupling Capacitor Placement at Board Level