Faculty Publications
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Item A High-Sensitive High-Input Impedance CMOS Front-End Amplifier for Neural Spike Detection(Springer Science and Business Media Deutschland GmbH, 2023) Naik, J.D.; Gorre, P.; Al-Shidaifat, A.D.; Kumar, S.; Song, H.Neural spikes detection and monitoring for neuro-prosthetic applications require an efficient and robust front-end amplifier (FEA), which regulates the fidelity of the neural signal. This paper presents neutralization and bootstrapping techniques to overcome the input leakage currents produced by amplifiers of the input bias network. In addition, a pseudo-resistor technique ensures the FEA maintains a high-input impedance. The CMOS-based FEA architecture is executed in the advanced design system with the design kit of the CMOS process. The proposed design achieves a high-input impedance of 0.5 TΩ with a maximum simulation gain of 66.2 dB. The overall power consumption of the topology is observed as 2.6 µW with a power supply voltage of 0.9 V. The simulated noise performance of 6 nV/√Hz at 1 kHz demonstrates a high-sensitive design compared to the previous works. It is highly recommended for succeeding neuro-prosthetic applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item High-Performance Graphene FET Integrated Front-End Amplifier Using Pseudo-resistor Technique for Neuro-prosthetic Diagnosis(SpringerOpen, 2022) Naik, J.D.; Gorre, P.; Akuri, N.G.; Kumar, S.; Al-Shidaifat, A.D.; Song, H.A complex analysis of spike monitoring in neuro-prosthetic diagnosis demands a high-speed sub-nanoscale transistors with an advanced device technologies. This work reports the high performance of Graphene field-effect transistor (GFET) based front-end amplifier (FEA) design for the neuro-prosthetic application. The 9 nm Graphene FET device is optimized by characterization of transconductance and drain current towards high sensitivity and small factor. The proposed GFET-based FEA with pseudo-resistor technique demonstrates very high-input impedance in Tera-ohms that nullify the input leakage current. Here, gain-bandwidth product and noise optimization of GFET FEA enhances the overall gain with negligible noise. The proposed design operates at low voltage, further reduces the power consumption, and achieves less chip area in sub-nano size so it could be more suitable for implantable devices. The GFET-based FEA architecture achieves an action potential spike of 1.4 µV while the local field potentials spike of 1.8 mV. The proposed architecture is implemented in Advanced Design System using the design kit of the GFET process. Power consumption of 3.14 µW is observed with a supply voltage of 0.9 V. The simulated and experimental results of the proposed design achieve an input impedance of 2 TΩ with excellent noise performance over a wideband of 13.85 MHz. The proposed work demonstrates better neural activity sensing when compared to the state-of-the-artwork, which could be highly beneficial for future neuroscientists. © 2022, The Korean BioChip Society.