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This research article examines a L‐shaped dielectrically modulated label free TFET (L‐DM‐TFET) biosensor for the purpose of detecting different biomolecules using a label‐free biosensing detection technique. The proposed structure allows for the recognition of biomolecules by modulating various electrical properties, such as the drain current, transconductance, and linearity parameters. The source region of the proposed TFET incorporates a SiGe (source)/Si (channel) heterojunction, utilizing a low bandgap material of SiGe. This heterojunction is employed to enhance the ON‐state current of the devices. The materials used and the fabrication steps involved in our proposed device are compatible with complementary metal‐oxide‐semiconductor (CMOS) technology. This analysis is conducted using a calibrated Silvaco technology computer‐aided design (TCAD) simulator. Additionally, by considering a dielectric constant range of 1–12, we calculate various figure of merits (FOMs) parameters for the device. These include evaluation of linearity, sensitivity, and noise characteristics. Furthermore, we have conducted an analysis of linearity FOMs, such as VIP2, VIP3, IIP3, and IMD3 for the proposed device under study. Additionally, the linearity analysis of the presented tunneling FET (TFET) indicates the device's excellent performance in distortionless switching operations. Consequently, the L‐shaped dielectrically modulated biosensor holds potential suitability for high‐speed circuit designs. [ABSTRACT FROM AUTHOR]

This work substantiates the impact of Gaussian doping on the electrical performance of double gate junctionless field-effect transistor (DG-JLFET). To get a better understanding of the influence of non-uniform doping, the device is compared with uniform-doped DG-JLFET with various concentrations. The device is later used to demonstrate its usability in six-transistor static random access memory (6T SRAM) bitcell by studying the performance metrics, i.e. stability noise margin and write delay. A comparison of performance metrics is also given with uniformly doped DG-JLFET-based-6T SRAM. Improvement in static noise margin was observed with Gaussian doping without compromising with write access time. [ABSTRACT FROM AUTHOR]

In recent years, wavelet packet (WP) transform has been used as an important speech representation tool. WP‐based acoustic features have found to be more effective than the short‐time Fourier transform (STFT)‐based features to capture the information of unvoiced phoneme in continuous speech. However, wavelet features fail to carry the same usefulness to represent the voiced phonemes such as vowels, nasals. This paper proposes new WP sub‐band‐based features by taking care of harmonic information of voiced speech signal. It has been noted that most of the voiced energy of the speech signal lies in between 250 and 2000 Hz. Thus, the proposed technique emphasises the individual sub‐band harmonic energy up to 2 kHz. The speech signal is decomposed into 16 wavelet sub‐bands and harmonic energy features are combined with WP cepstral (WPCC) features to enhance the performance of voiced phoneme recogniser. A standard phonetically balanced Hindi database is taken to analyse the performance of the proposed feature set. The noisy phoneme recognition task is also carried out to study the robustness. Significant improvement is obtained with the proposed feature set in voiced phoneme recognition over WPCC and conventional Mel frequency cepstral coefficient. [ABSTRACT FROM AUTHOR]