Automation of parameter extraction procedure for Si JFET SPICE model in the −200…+110°C temperature range

Petrosyants, Konstantin O.; Ismail-zade, Mamed R.; Sambursky, Lev M.; Dvornikov, O.; Lvov, Boris G.; Kharitonov, I. A.

doi:10.1109/mwent.2018.8337212

Cited by 13 publications

(3 citation statements)

References 15 publications

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“…It took 23.9 h to generate the data needed for the sensitivity analysis and 36 s for the sensitivity analysis itself. The extraction of device model parameters needs to take into account various working conditions (voltages, temperature) [14,15] and use a MOSFET of different sizes [16,17]. Therefore, the inputs of the NN need to contain six variables, namely, drain-source voltage (V ds ), gate-source voltage (V gs ), bulk-source voltage (V bs ), temperature (T), gate width (W), and gate length (L) in addition to the model parameters.…”

Section: Data Preparationmentioning

confidence: 99%

Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction

Kang

Chen

et al. 2022

Applied Sciences

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The parameter extraction of device models is critically important for circuit simulation. The device models in the existing parameter extraction software are physics-based analytical models, or embedded Simulation program with integrated circuit emphasis (SPICE) functions. The programming implementation of physics-based analytical models is tedious and error prone, while it is time consuming to run the device model evaluation for the device model parameter extraction software by calling the SPICE. We propose a novel modeling technique based on a neural network (NN) for the optimal extraction of device model parameters in this paper, and further integrate the NN model into device model parameter extraction software. The technique does not require developers to understand the device model, which enables faster and less error-prone parameter extraction software developing. Furthermore, the NN model improves the extraction speed compared with the embedded SPICE, which expedites the process of parameter extraction. The technique has been verified on the BSIM-SOI model with a multilayer perceptron (MLP) neural network. The training error of the NN model is 4.14%, and the testing error is 5.38%. Experimental results show that the trained NN model obtains an extraction error of less than 6%, and its extraction speed is thousands of times faster than SPICE in device model parameter extraction.

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Section: Data Preparationmentioning

confidence: 99%

Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction

Kang

Chen

et al. 2022

Applied Sciences

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“…Recently, we have carried out the studies both on the development of radiation-hardened structured arrays (SA) and array chips (AC) [23 − 26], and on the creation of a complex of design tools for cryogenic analog ICs [27,28].…”

Section: Introductionmentioning

confidence: 99%

Design of low-temperature DDOAs on the elements of BiJFet array chip MH2XA030

Dvornikov¹,

Tchekhovski

Dziatlau

et al. 2018

Serb J Electr Eng

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Brief information about the new BiJFet array chip (AC) MH2XA030 intended for accelerated creation of analog integrated circuits (IC), which retain their performance under the influence of penetrating radiation and extremely low temperatures (up to minus 197 °С) is presented. The features of schematic design of two types of DDOAs (OAmp3, OAmp4) are considered. The recommendations on the schematic design of the DDOA are developed taking into account the static characteristics of the field effect and bipolar transistors of the AC under the influence of low temperatures. The amplitude-frequency response of the DDOA and the dependence of the noise voltage on the frequency of Fourier density are given. At a temperature of-197°С cryogenic amplifiers OAmp3 (OAmp4) are characterized by the following parameters: the current consumption is less than 500 μA, the input current is less than 1 fA, the voltage gain is more than 50.000 (200.000), the offset voltage is less than 200 (60) μV. The results of the circuit simulation of the instrumentation amplifier based on DDOA OAmp3 are presented.

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“…Ранее КН были разработаны для микросхем базового структурного кристалла (БСК) [3] МН2ХА010 [4][5][6] и БМК АБМК-2.1 [4,7], обеспечивающих высокий уровень электрических параметров полузаказных ИС [8,9]. Исследования [10][11][12][13][14][15][16][17]…”

Section: Introductionunclassified

Design of Voltage Comparators Based on the Elements of the Radiation-Hardened Low-Temperature BiJFET Array Chip MH2XA030

Дворников¹,

Tchekhovski²,

Дятлов³

et al. 2018

MES

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Automation of parameter extraction procedure for Si JFET SPICE model in the −200…+110°C temperature range

Cited by 13 publications

References 15 publications

Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction

Research on Device Modeling Technique Based on MLP Neural Network for Model Parameter Extraction

Design of low-temperature DDOAs on the elements of BiJFet array chip MH2XA030

Design of Voltage Comparators Based on the Elements of the Radiation-Hardened Low-Temperature BiJFET Array Chip MH2XA030

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