Sub-<inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math> </inline-formula>V_rms-Noise Sub-<inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math> </inline-formula>W/Channel ADC-Direct Neural Recording With 200-mV/ms Transient Recovery Through Predictive Digital Autoranging

Abstract

Integrated recording of neural electrical potentials from the brain poses great challenges due to stringent dynamic range requirements to resolve small-signal amplitudes buried in noise amidst large artifact and stimulation transients, as well as stringent power and volume constraints to enable minimally invasive untethered operation. Here, we present a 16-channel neural recording system-on-chip with greater than 90-dB input dynamic range and less than 1-µV rms input-referred noise from dc to 500 Hz, at 0.8-µW power consumption, and 0.024-mm 2 area per channel in a 65-nm CMOS process. Each recording channel features a hybrid analog-digital second-order oversampling analog-to-digital converter (ADC), with the biopotential signal coupling directly to the second integrator for high conversion gain and dynamic offset subtraction in the digital domain. This bypasses the need for high-pass filtering pre-amplification in neural recording systems, which often leads to signal distortion. The integrated ADC-direct neural recording offers record figureof-merit with a noise efficiency factor (NEF) of the combined front end and ADC of 1.81, and a corresponding power efficiency factor (PEF) of 2.6. Predictive digital autoranging of the binary quantizer further supports rapid transient recovery while maintaining fully dc-coupled operation. Hence, the neural ADC is capable of recording ≤0.01-Hz slow potentials as well as recovering from ≥200-mV pp transients within ≤1 ms that are important prerequisites to effective electrocortical recording for brain activity mapping. In vivo recordings from marmoset primate frontal cortex demonstrate its unique capabilities in resolving ultra-slow local field potentials indicative of subject arousal state. Index Terms-analog-to-digital converter (ADC)-direct front end, artifact recovery, autoranging, digital prediction, high dynamic range ADC, neural ADC, neural interfaces.

Energy-Efficient Integrated Circuit Solutions Toward Miniaturized Closed-Loop Neural Interface Systems

“…This structure is called 2 ADC . (C) An auto-ranging quantizer in 2 makes it possible to follow sudden changes in the input signal (Kim et al, 2018).…”

“…signal to the integrator in main path by around 30 dB Bang et al, 2018;Kim et al, 2018;Reza Pazhouhandeh et al, 2020). Furthermore, the integrator in the main path can be implemented by a low-power open-loop Gm-C filter owing to its small input, allowing for drastic power reduction.…”

“…This is a serious problem in closedloop stimulation since the stimulation artifact is large and there is steep variation in amplitude. Actually, utilizing history of the quantizer outputs, ADC detects whether it is slower than input by itself (Kim et al, 2018). If several consecutive outputs of the quantizer, D out , have an equal sign, an ADC needs to tract its input faster, leading to an increase in the update size in the prediction.…”

Research of Continuous Delta-Sigma Analog-to-Digital Converter for Bio-Electricity Signal Acquisition

“…In recent years, portable wearable bio-electricity signal monitoring devices are playing an significant part in the field of modern medicine [1,2]. A bio-electricity signal acquisition system usually consists of an analog front-end amplifier and an analog-to-digital converter (ADC).…”

Compact Continuous Time Common-Mode Feedback Circuit for Low-Power, Area-Constrained Neural Recording Amplifiers

“…In particular, an AC-coupled closed-loop amplifier that is one of the most widely used neural recording amplifiers must have a high-performance OTA inside because it mainly determines the overall performance necessary for neural recordings, such as low input-referred noise (IRN), large bandwidth, low power, and small area consumptions, acceptable input/output signal ranges, and low distortion. Recent state-of-the-art neural recording amplifiers have been extensively explored by adopting various OTA topologies, such as a current mirror [8], two-stage [4,5,[16][17][18], and folded cascode (FC) OTAs [9,[19][20][21], in addition to the novel circuit design techniques [13,[20][21][22][23]. Among the aforementioned OTA topologies, the FC-OTA has demonstrated that it reached the theoretical noise efficiency factor (NEF) limit of roughly 2 (~2.67), without adding the special circuit design techniques such as pre-amplification, current-reuse, and body biasing, and with the simple adaptation of large current scaling ratio and source degeneration [9].…”