A relation between dynamic saturation characteristics and tail current of nonpunchthrough IGBT

Yamashita, J.; Yamada, Takayuki; Uchida, Satoshi; Yamaguchi, Hideyuki; Ishizawa, S.

doi:10.1109/ias.1996.559252

Cited by 14 publications

(2 citation statements)

References 5 publications

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“…For this reason, IGBTs are widely used as power semiconductors, and super-junction IGBTs (SJBTs) have an especially high BV caused by fully depleted pillars [ 11 ]. Despite these advantages, the injected holes in the p-pillar in the turn-off state become a problem in that they increase the turn-off loss ( E off ) [ 12 , 13 ]. As soon as the device is turned off, the holes in the p-pillar cannot be extracted immediately, causing loss.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT

et al. 2023

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An improved structure for an Insulated Gate Bipolar Transistor (IGBT) with a separated buffer layer is presented in order to improve the trade-off between the turn-off loss (Eoff) and on-state voltage (Von). However, it is difficult to set efficient parameters due to the increase in the new buffer doping concentration variable. Therefore, a machine learning (ML) algorithm is proposed as a solution. Compared to the conventional Technology Computer-Aided Design (TCAD) simulation tool, it is demonstrated that incorporating the ML algorithm into the device analysis could make it possible to achieve high accuracy and significantly shorten the simulation time. Specifically, utilizing the ML algorithm could achieve coefficients of determination (R2) of Von and Eoff of 0.995 and 0.968, respectively. In addition, it enables the optimized design to fit the target characteristics. In this study, the structure proposed for the trade-off improvement was targeted to obtain the minimum Eoff at the same Von, especially by adjusting the concentration of the separated buffer. We could improve Eoff by 36.2% by optimizing the structure, which was expected to be improved by 24.7% using the ML approach. In another way, it is possible to inversely design four types of structures with characteristics close to the target characteristics (Eoff = 1.64 μJ, Von = 1.38 V). The proposed method of incorporating machine learning into device analysis is expected to be very strategic, especially for power electronics analysis (where the transistor size is comparatively large and requires significant computation). In summary, we improved the trade-off using a separated buffer, and ML enabled optimization and a more precise design, as well as reverse engineering.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT

et al. 2023

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“…Therefore, the resistance in the pillar is reduced by conductivity modulation [15], and the on-state voltage (V on ) decreases. However, the increased minority carriers in each pillar induce a tail current [16,17], which cannot be extracted quickly at the off-state. Then it deteriorates the turn-off loss (E off ).…”

Section: Introductionmentioning

confidence: 99%

Improvement in Turn-Off Loss of the Super Junction IGBT with Separated n-Buffer Layers

et al. 2021

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In this study, we propose a super junction insulated-gate bipolar transistor (SJBT) with separated n-buffer layers to solve a relatively long time for carrier annihilation during turn-off. This proposition improves the turn-off characteristic while maintaining similar on-state characteristics and breakdown voltage. The electrical characteristics of the devices were simulated by using the Synopsys Sentaurus technology computer-aided design (TCAD) simulation tool, and we compared the conventional SJBT with SJBT with separated n-buffer layers. The simulation tool result shows that turn-off loss (Eoff) drops by about 7% when on-state voltage (Von) and breakdown voltage (BV) are similar. Von increases by about 0.5% and BV decreases by only about 0.8%.

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A new punch-through IGBT having a new n-buffer layer

Iwamoto

Haruguchi

Tomomatsu³

et al. 2002

IEEE Trans. on Ind. Applicat.

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A relation between dynamic saturation characteristics and tail current of nonpunchthrough IGBT

Cited by 14 publications

References 5 publications

Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT

Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT

Improvement in Turn-Off Loss of the Super Junction IGBT with Separated n-Buffer Layers

A new punch-through IGBT having a new n-buffer layer

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