Ga₂O₃ has gained intensive attention for the continuing myth of the electronics as a new-generation wide bandgap semiconductor, owing to its natural physical and chemical properties. In this review article, we selectively summarized the recent advances on the experimental and theoretical demonstration of β-Ga₂O₃-based power devices, including Schottky barrier diodes and field-effect transistors, aiming for an inherent comprehending of the operating mechanisms, discussion on the obstacles to be addressed, and providing some comprehensive guidance for further developments. In the short run, Ga₂O₃ may well be promising to lead power electronics.

The breakdown performance is a critical metric for power device design. This paper explores the feasibility of efficiently predicting the breakdown performance of silicon on insulator (SOI) lateral power device using multi-layer neural networks as an alternative to expensive technology computer-aided design (TCAD) simulation. In this work, we propose the first breakdown performance prediction framework, PowerNet, for SOI lateral power devices, based on deep learning methods. The framework can provide breakdown location prediction and breakdown voltage (BV) prediction by utilizing a two-stage machine learning method. In addition, it demonstrates 97.67% accuracy on breakdown location prediction and less than 4% average error on the BV prediction compared with TCAD simulation. The proposed method can be used to measure changes in performance caused by random variability in structural parameters during manufacturing process, allowing designers to avoid unstable structural parameters and enhance design robustness. More importantly, it can significantly reduce the computational cost when compared with the TCAD simulation. We believe the proposed machine learning technique can significantly speedup the design space exploration for power devices, eventually reducing the overall product-to-market time.

The breakdown characteristics of a practical silicon-on-insulator (SOI) lateral power device are generally limited by the 3-D curvature effect induced by its circular layout. The Conventional 2-D design methods such as 2-D variation of lateral doping (VLD) technique cannot derive optimal device parameters. In this paper, for the first time, a 3-D VLD technique is proposed to suppress the 3-D curvature effect in the lateral power device with circular layout. The 3-D Poisson equation is solved to formulate the optimized doping profile. TCAD tools are employed to verify the model and explore the physic insight. By adding an inversely proportional doping profile onto the conventional linear doping profile, the analytical and numerical results show that the uniform electric field in the drift region and maximized breakdown voltage (BV) are obtained for the SOI lateral double-diffused metal-oxide-semiconductor with circular layout. Furthermore, compared to the 2-D VLD device, the proposed 3-D VLD technique also contributes to excellent on-state characteristics, including the high on-state BV, reduced specific on-resistance, large saturation current, suppressed quasi-saturation effect, improved transconductance, and thus a large Baliga's figure of merits (BFOM).

The selection of design structure parameters is a critical step for meeting the performance requirement for silicon on insulator (SOI) lateral power devices, especially when multiple design constraints are presented. In this letter, we propose a fully-automated structural design method for SOI lateral power device based on a Bayesian optimization (BO) framework. Given the design target characterized by breakdown voltage (BV) and on-resistance (Ron) specifications, the proposed approach searches for the optimal structure that can satisfy the constraints. The experimental results demonstrate that designs obtained from our optimization framework fall within 5% range from the desired specifications when evaluated using technology computer-aided design (TCAD) simulation, which proves the efficiency of the proposed approach. Besides, the efficiency of our proposed approach is reflected by its runtime which does not exceed 10 minutes for more than 70% of the cases. We believe that this modeling method can greatly accelerate the design exploration process of power devices for designers.

Double-reduced surface field (D-RESURF) technique aims to increase the doping concentration of drift regions and maintain a high breakdown voltage. However, conventional 2-D models are too complicated and unable to elaborate its physical meaning. Hence, the D-RESURF effective doping concentration (EDC) theory is proposed in this paper to explore the physical insight of the D-RESURF effect by equating the sophisticated 2-D structure to a simple 1-D RESURF model with segmented-doped p-n junction. The EDC indicates that an NPNP structure may exist because of the influence of the P-top region. Thus, two electric field valleys and one electric field peak can be formed on the surface. Based on the theory, a 1-D analytical model is presented to qualitatively and quantitatively explore the impact of D-RESURF effect on breakdown mechanism of silicon on insulator lateral double diffusion MOS. The results obtained by the proposed model are found to be sufficiently accurate comparing with TCAD simulation results.

The application of organic field-effect transistors (OFETs) in the power device realm has not been explored yet. To achieve a high breakdown voltage, a novel lateral drift region OFET(LDR-OFET) is proposed in this letter. By employing the diketopyrrolopyrrole-based polymer(DPPT-TT) and Poly (methyl methacrylate) (PMMA) to form semiconductor and insulator layers respectively, the fabricated LDR-OFET, despite the simple process and low cost, exhibits an excellent off-state breakdown capability while maintaining a considerable on-state performance. With a $20~\mu \text{m}$ length of the drift region, the LDR-OFET realizes 2200V breakdown voltage on a spin-coated 50nm thick semiconductor layer. The average electric field of the proposed device reaches 1.18MV/cm before an avalanche-like breakdown occurs. The physical nature of the breakdown mechanism is explored using the 1-D PIN equivalent structure and avalanche breakdown theory. The correctness and effectiveness of the proposed LDR-OFET and breakdown analysis are well demonstrated and validated by experimental results. The LDR-OFETs are expected to fill the blank of organic power devices and be the core component of future organic power integrated circuits.