A LowNoise High Input Impedance Analog FrontEnd Design for Neural Recording Implant This paper presents a high input impedance, lownoise, and powerefficient analog frontend (AFE) for neural recording implant. A lownoise, lowpower input buffer is used as the input block of the AFE to achieve a high input impedance (over  700 mathrmGOmega   0.6 Hz). The buffer is followed by a capacitivelycoupled instrumentation amplifier (CCIA) in the second stage to improve the CommonMode Rejection Ratio (CMRR) and the inputreferred noise of the design. A new chopper stabilization control technique is proposed and used in the CCIA stage to reduce the charge injection and clock feedthrough and consequently the highfrequency ripple of the AFE output signal. A programmable gain amplifier (PGA) is designed as the third stage to adjust the overall gain of the AFE. Benefiting from PGA, the AFE is able to adapt its gain with both action potential and local field potential signals. The proposed AFE is designed and simulated using standard TSMC 180nm CMOS process and operates in a wide frequency band of 0.6 Hz to 1000 Hz with low inputreferred noise of  2.4 mutextVrms  and a CMRR over 100 dB at 0.6 Hz. The total power consumption is lower than  5.5 mu mathrmW  per channel. By applying the proposed chopper stabilization control technique, the amplitude of highfrequency output ripple is reduced by 45% compared to the conventional switched control.

MultiChannel Neural Recording Implants: A Review The recently growing progress in neuroscience research and relevant achievements, as well as advancements in the fabrication process, have increased the demand for neural interfacing systems. Brainxe2x80x93machine interfaces (BMIs) have been revealed to be a promising method for the diagnosis and treatment of neurological disorders and the restoration of sensory and motor function. Neural recording implants, as a part of BMI, are capable of capturing brain signals, and amplifying, digitizing, and transferring them outside of the body with a transmitter. The main challenges of designing such implants are minimizing power consumption and the silicon area. In this paper, multichannel neural recording implants are surveyed. After presenting various neuralsignal features, we investigate main available neural recording circuit and system architectures. The fundamental blocks of available architectures, such as neural amplifiers, analog to digital converters (ADCs) and compression blocks, are explored. We cover the various topologies of neural amplifiers, provide a comparison, and probe their design challenges. To achieve a relatively high SNR at the output of the neural amplifier, noise reduction techniques are discussed. Also, to transfer neural signals outside of the body, they are digitized using data converters, then in most cases, the data compression is applied to mitigate power consumption. We present the various dedicated ADC structures, as well as an overview of main data compression methods.