Nature India has highlighted Prof. Santanu Mahapatra work on energy storage in 2d #semi

Nature India has highlighted Prof. Santanu Mahapatra work on energy storage in two dimension #india #semi #2D #2dmaterials https://t.co/wPsqmK9GuW

— Wladek Grabinski (@wladek60) Jan 28, 2022

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January 28, 2022 at 01:50PM
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Sam Zeloof talks about his 1200 transistor chip

[Naveed Sherwani] Please watch the video, in which Sam Zeloof talks about his 1200 transistor chip #semi https://t.co/QZpqStfZ7A

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January 28, 2022 at 01:54PM
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[Book] "GaN power devices and applications" edited by Alex Lidow, founder and CEO of GaN power transistor maker EPC https://t.co/nKeKsg83nJ #semi https://t.co/JPP8H6Qvyt

[Book] "GaN power devices and applications" edited by Alex Lidow, founder and CEO of GaN power transistor maker EPC https://t.co/nKeKsg83nJ #semi https://t.co/JPP8H6Qvyt

— Wladek Grabinski (@wladek60) Jan 27, 2022

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January 27, 2022 at 08:24PM
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FOSDEM 2022 - Schedule https://t.co/RcJVmLrk52 * Libre-Open VLSI and FPGA devroom https://t.co/bgmsQJOTQy * Computer Aided Modeling and Design devroom https://t.co/blTQbn0cro #semi #SPICE #EDA #VLSI #FPGA #IC #Desing https://t.co/VqFiEuI8jH

FOSDEM 2022 - Schedule https://t.co/RcJVmLrk52 * Libre-Open VLSI and FPGA devroom https://t.co/bgmsQJOTQy * Computer Aided Modeling and Design devroom https://t.co/blTQbn0cro #semi #SPICE #EDA #VLSI #FPGA #IC #Desing https://t.co/VqFiEuI8jH

— Wladek Grabinski (@wladek60) Jan 27, 2022

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January 27, 2022 at 08:09PM
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[paper] Automatic Parameter Extraction of MOSFET Compact Models

Gazmend Alia^1,2, Andi Buzo¹, Hannes Maier-Flaig¹, Klaus-Willi Pieper¹, 

Linus Maurer²  and Georg Pelz¹

Automatic Parameter Extraction of MOSFET Compact Models using Differential Evolution with Population Prediction (DEpred)

6th EDTM; March 6 to 9, 2022 

   
1 Infineon Technologies AG, Munich (D)
2 Bundeswehr University Munich (D)

Abstract: Parameter extraction of MOSFET compact models with hundreds of parameters is not a trivial task. Differential evolution (DE) has proven to be very effective in such highly dimensional parameter spaces. However, DE needs a large number of iterations to converge. This paper proposes a novel method to accelerate the convergence of DE by predicting tens of iterations ahead where the population will be, based on the knowledge from the already finished iterations. The method is validated with BSIM4 and HiSIM-HV compact models, where up to 50% of the iterations are saved.

Fig: DE vs DEpred cost function for BSIM4 and HiSIM-HV models.
DEpred reaches the target 50% faster.