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This paper presents the hybridized fuzzy-neural predictive intelligent (HFNPI) based approach for predicting FinFET model. The proposed model is an improved approach over artificial neural network (ANN) model and adaptive neuro fuzzy inference system (ANFIS) model that provides optimum model parameters. In this paper, the challenge of optimization of large number of parameters is reduced. It assesses the performance of resultant structure on the basis of selected RF figure-of-merit. Based on the application requirements, RF figure-of-merit such as fT and fmax are used as appropriate performance parameters to analyse the device performance. The framework for the HFNPI model is underlapped FinFET structural/process parameters. The mean prediction error was found 2.8 × 10−4 GHz. The comparative analysis of HFNPI with ANN and ANFIS model is done. It is found that combination of fuzzy and neural logic shows better result rather than individual ANFIS and ANN model to redevelop the model and increase the number of inputs. [ABSTRACT FROM AUTHOR]

In this study, a 12-bit 65MS/s MOSFET-only pipelined analogue-to-digital converter (ADC) is successfully designed and implemented in 0.18 µm standard digital complementary metal–oxide–semiconductor (CMOS) technology. In the proposed ADC, constant capacitors of the analogue pipeline stages are replaced with anti-parallel depletion-mode metal–oxide–semiconductor (MOS) devices and their non-linearity is modelled and calibrated digitally. Simulations show that the proposed digital calibration algorithm improves the ADC signal-to-noise plus distortion ratio and spurious-free dynamic range from 43 dB and 44 dB to 70 dB and 78 dB, respectively. [ABSTRACT FROM AUTHOR]