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This paper presents a power-efficient complementary metal-oxide-semiconductor (CMOS) neural signal-recording read-out circuit for multichannel neuromodulation implants. The system includes a neural amplifier and a successive approximation register analog-to-digital converter (SAR-ADC) for recording and digitizing neural signal data to transmit to a remote receiver. The synthetic neural signal is generated using a LabVIEW myDAQ device and processed through a LabVIEW GUI. The read-out circuit is designed and fabricated in the standard 0.5 μμm CMOS process. The proposed amplifier uses a fully differential two-stage topology with a reconfigurable capacitive-resistive feedback network. The amplifier achieves 49.26 dB and 60.53 dB gain within the frequency bandwidth of 0.57–301 Hz and 0.27–12.9 kHz to record the local field potentials (LFPs) and the action potentials (APs), respectively. The amplifier maintains a noise–power tradeoff by reducing the noise efficiency factor (NEF) to 2.53. The capacitors are manually laid out using the common-centroid placement technique, which increases the linearity of the ADC. The SAR-ADC achieves a signal-to-noise ratio (SNR) of 45.8 dB, with a resolution of 8 bits. The ADC exhibits an effective number of bits of 7.32 at a low sampling rate of 10 ksamples/s. The total power consumption of the chip is 26.02 μμW, which makes it highly suitable for a multi-channel neural signal recording system.
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A Low-Power Asynchronous Level Crossing ADC designed in 180nm CMOS process for Electrophysiological Signal Recording Applications
Presented in this paper is the design of a level-crossing ADC for biomedical potentials. This architecture takes advantage of the time sparse nature of neural signal recording applications by only sampling when the signal is moving. A 10-bit architecture with a novel threshold control scheme was chosen to help capture both the higher amplitude local field potentials and lower amplitude action potentials found in these systems. The ADC operates on a power of 13.5μW from a 1.8 V supply and achieves a root-mean-square error (RMSE) of 0.65 mV. The design is implemented and simulated in a 180 nm CMOS process using the Cadence Virtuoso Custom IC design tool.
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- Award ID(s):
- 1943990
- PAR ID:
- 10394661
- Date Published:
- Journal Name:
- 2022 IEEE 15th Dallas Circuit And System Conference (DCAS)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)This paper presents a fully reconfigurable readout circuit including a chopper-stabilized neural amplifier and a successive approximation register (SAR) analog-to-digital converter (ADC) for neural signal recording applications. Since the target neural signals - action potentials (APs) and local field potentials (LFPs) differ in the peak amplitude while occupying different frequency bandwidths, gain, and bandwidth reconfigurability would be advantageous in improving power and noise performance. The readout circuit is designed in 180 nm standard CMOS technology. It achieves the mid-band gain of 50.3 dB in the frequency band of 0.1 Hz - 250 Hz to detect the LFPs, and 63.4 dB in 267 Hz - 20.8 kHz for detecting the APs. The neural amplifier consumes a total power of 1.54 μW and 1.94 μW for LFP and AP configurations, respectively. The input-referred noises have been achieved as 0.97 μV rms (0.1 Hz - 250 Hz), and 0.44 μV rms (250 Hz - 5 kHz), leading to a noise efficiency factor (NEF) of 1.27 and 1.21, for the two configurations, respectively. It rejects the generated large DC offset up to 40 mV at the electrode-tissue interface, by implementing a DC servo loop (DSL). The offset voltage with the DSL becomes 0.23 mV, which is acceptable for the neural experiments. Enabling the impedance boosting loop, the DC input impedance is found to be within the range of 1.77 - 2.27 GΩ, introducing the reconfigurability in impedance for matching with the electrode impedance. The SAR-ADC having a varying sampling frequency ranging from 10 - 40 ksamples/s demonstrates to digitize the APs and the LFPs with the resolution from 8 - 10 bits. The entire AFE provides good compatibility to record the neural signal while lowering the large DC offset down to 0.23 mV.more » « less
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In this paper, a monotonic power side-channel attack (PSA) is proposed to analyze the security vulnerabilities of flash analog-to-digital converters (ADC), where the digital output of a flash ADC is determined by characterizing the monotonic relationship between the traces of the power consumed and the applied input signals. A novel technique that leverages clock phase division is proposed to secure the power side channel information of a 4-bit flash ADC. The proposed technique adds randomness to decorrelate the input signal from the given power trace as the execution phase of each comparator depends on a thermometer code computed from the previous seven clock cycles. The monotonic PSA is executed on both a secured and unsecured ADC, with results indicating 1.9 bits of information leakage from an unprotected ADC and no data leakage from a protected ADC as the bit-wise accuracy is approximately 50% when secured. The monotonic PSA is more effective at attacking a flash ADC architecture than either a convolutional neural network based PSA or a correlation template PSA. The secured ADC core occupies approximately 2% more area than a non-secure ADC in a 65 nm process, and provides a sampling frequency of up to 500 MHz at a supply voltage of 1.2 V. Index Terms—power side-channel, ADC,more » « less
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SALT, a new dedicated readout Application Specific Integrated Circuit (ASIC) for the Upstream Tracker, a new silicon detector in the Large Hadron Collider beauty (LHCb) experiment, has been designed and developed. It is a 128-channel chip using an innovative architecture comprising a low-power analogue front-end with fast pulse shaping and a 40 MSps 6-bit Analog-to-Digital Converter (ADC) in each channel, followed by a Digital Signal Processing (DSP) block performing pedestal and Mean Common Mode (MCM) subtraction and zero suppression. The prototypes of SALT were fabricated and tested, confirming the full chip functionality and fulfilling the specifications. A signal-to-noise ratio of about 20 is achieved for a silicon sensor with a 12 pF input capacitance. In this paper, the SALT architecture and measurements of the chip performance are presented.more » « less
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Abstract Objective.Intan Technologies’ integrated circuits (ICs) are valuable tools for neurophysiological data acquisition, providing signal amplification, filtering, and digitization from many channels (up to 64 channels/chip) at high sampling rates (up to 30 kSPS) within a compact package (⩽9× 7 mm). However, we found that the analog-to-digital converters (ADCs) in the Intan RHD2000 series ICs can produce artifacts in recorded signals. Here, we examine the effects of these ADC artifacts on neural signal quality and describe a method to detect them in recorded data.Approach.We identified two types of ADC artifacts produced by Intan ICs: 1) jumps, resulting from missing output codes, and 2) flatlines, resulting from overrepresented output codes. We identified ADC artifacts in neural recordings acquired with Intan RHD2000 ICs and tested the repeated performance of 17 ICsin vitro. With the on-chip digital-signal-processing disabled, we detected the ADC artifacts in each test recording by examining the distribution of unfiltered ADC output codes.Main Results.We found larger ADC artifacts in recordings using the Intan RHX data acquisition software versions 3.0–3.2, which did not run the necessary ADC calibration command when the inputs to the Intan recording controller were rescanned. This has been corrected in the Intan RHX software version 3.3. We found that the ADC calibration routine significantly reduced, but did not fully eliminate, the occurrence and size of ADC artifacts as compared with recordings acquired when the calibration routine was not run (p< 0.0001). When the ADC calibration routine was run, we found that the artifacts produced by each ADC were consistent over time, enabling us to sort ICs by performance.Significance.Our findings call attention to the importance of evaluating signal quality when acquiring electrophysiological data using Intan Technologies ICs and offer a method for detecting ADC artifacts in recorded data.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DCAS53974.2022.9845498
