This letter proposes an advanced neural space mapping (NSM)-based inverse modeling method and its applications to microwave filter design. For the first time, the NSM method is introduced into inverse microwave modeling with input dimensional reduction (IDR). By using the Fourier transform and its low-frequency subspaces, we convert the S-parameter curve into a signal spectrum where the energy is concentrated in the low-frequency range, to reduce the dimension of the inverse model. We also propose a two-stage training algorithm for the NSM-based inverse model, along with its application methodology for microwave filter design. Two microwave filter design examples are presented to demonstrate the feasibility of the proposed method.

This letter proposes a novel electromagnetic (EM) optimization approach using adjoint-sensitivity-based multifeature surrogate for microwave filter design. The proposed technique aims to solve the EM optimization problem, in which the starting point is far from the design specifications. We propose to add feature sensitivities in multifeature surrogate modeling to achieve a more accurate surrogate compared with the existing methods without feature sensitivities. We also propose a new design objective function in feature space to further improve the efficiency of multifeature-assisted optimization. Two microwave filter examples are used to demonstrate the proposed technique.

High-efficiency, high-speed phototransistors applicable in visible wavelength and the near-infrared regime are highly desirable for optical communication links utilized for data centers, high-performance computing, and laser radar application. To overcome the intrinsic weak absorption of silicon material and alleviate the compromise between efficiency and speed of the device, a SiGe/Si heterojunction phototransistor (HPT) with photon-trapping nanoholes is demonstrated in this article. With photon-trapping nanohole structures, the absorption efficiency of the HPT is greatly improved near the whole band ranging from 600- to 1000-nm wavelength compared to one without nanoholes. The responsivity of nanohole-assisted HPT is 25.69, 27.02, and 15.65 A/W at 650-, 850-, and 940-nm wavelength, exhibiting 85.22%, 192.42%, and 848.48% improvement compared with its counterpart without nanoholes. The thin base region also allows a high speed of the HPT with a transient response of 0.45 ns at 850-nm wavelength.

Artificial neural network (ANN) model development for microwave components principally includes two parts of work, i.e., data sampling and model structure adaptation. In existing various ANN modeling methods, the model structure adaptation process mainly focuses on adjusting the number of neurons within each hidden layer of ANN while keeping the number of layers unchanged. To make the ANN modeling process more flexible and efficient, an automated multilayer neural network structure adaptation method with $ {l_{1}}$ regularization is proposed in this letter. We propose a new ANN model structure combining multilayer perceptron (MLP) and additional connections between the output layer and each hidden layer/input layer. A new training scheme with $ {l_{1}}$ regularization is proposed to automatically determine the final model structure with user-desired model accuracy. Using the proposed model structure adaptation method, both the number of layers and the number of neurons within each layer of the final ANN model can be adaptively determined to address different needs for different microwave modeling problems. The proposed method is demonstrated by two microwave filter modeling examples in which the model development process achieves a time saving of at least 40% over existing methods.

This study investigates the thermal design of 2-D vertical-cavity surface-emitting laser (VCSEL) arrays for optical output power improvement. Considering the temperature dependencies of bias current, internal quantum efficiency, internal optical loss, and thermal resistance of each array cell, a compact electro-opto-thermal model of 2-D VCSEL arrays with linear power dissipation and quadratic power dissipation in each array cell is established to study the effects of array cell spacing and layout on the device performance. At the same time, the power dissipation mechanisms contributing to thermal rollover are also studied. It is found that the array cell spacing has a strong influence on the linear power dissipation, and the layout (such as hexagonal design) influences quadratic power dissipation obviously. With the careful design of array cell spacing and layout, both the temperature profile and the thermal resistance matrix of VCSEL arrays are improved, which is helpful to delay the onset of thermal rollover and hence enhance the optical output power. The simulation results are consistent with experimental results.

Artificial neural network (ANN) is a powerful technique in the microwave modeling area. The ANN structure adaptation in existing automated model generation (AMG) algorithms focuses on adjusting either the number of hidden layers or the number of hidden neurons, or adjusting both simultaneously but in a layer-by-layer manner. In this letter, a novel batch-adjustment algorithm for ANN structure adaptation is proposed to improve the ANN modeling efficiency. We introduce a cascaded multilayer ANN structure and propose a new training algorithm to train it. The ANN structure is automatically adjusted by adding or removing the redundant hidden layers in batch and makes a compromise between the number of hidden layers and the number of hidden neurons simultaneously. Compared to existing algorithms, our proposed algorithm is more flexible and efficient in ANN structure adaptation by performing fewer times of ANN training during the model development. Two microwave modeling examples are used to demonstrate the proposed algorithm.