# A 13.44-Bit Low-Power SAR ADC for Brain-Computer Interface Applications
## 1 Introduction  
As a cutting-edge neural engineering technology, brain-computer interface (BCI) systems have demonstrated revolutionary potential across medical rehabilitation, motor function restoration, and neuroscience research. Figure 1 illustrates representative electrophysiological signal modalities including electrocorticography (ECoG), action potentials (APs), electroencephalography (EEG), electrocardiograms (ECG), and local field potentials (LFPs) [1]. EEG-based systems not only decode neural patterns for device control, but also provide a versatile platform for brain state monitoring--enabling detection of physiological disorders (e.g., Parkinson's disease, epilepsy [2]) and psychological conditions (e.g., depression, insomnia [3]), with emerging applications extending to sleep staging [4], emotion recognition [5], and human-computer interaction. Clinically, implantable BCI systems enable robotic arm control for spinal cord injury patients through ECoG signal decoding, while non-invasive EEG systems establish novel communication pathways for locked-in syndrome patients. The translational value of such biosignal analysis is further evidenced in ECG monitoring, where cardiovascular diseases (CVDs) accounting for 31% of global mortality (17.9 million annual deaths) [6, 7] drive the demand for multimodal diagnostic platforms. These cross-domain applications impose three critical specifications on front-end circuits: (1) microwatt-scale ultra-low power consumption for implantability and continuous monitoring, (2) high quantization accuracy for neural spike detection andpathological biomarker identification, and (3) millimeter-scale miniaturized packaging to support ubiquitous health sensing. Within the BCI signal chain (Figure 2), the ADC serves as a pivotal interface between analog biopotentials and digital processing. Typical neural signals exhibit distinctive characteristics: 0.1-10 kHz bandwidth, 10-100 \(\upmu\)V amplitude, \(\pm\)300 mV electrode offset tolerance requiring high input impedance, and \(<\)10 dB signal-to-noise ratio (SNR) in harsh environments [8]. These constraints demand ADCs achieving 14-16-bit effective resolution with sub-\(\upmu\)W power budgets--a stringent requirement conflicting with traditional architectural paradigms. Current BCI systems primarily evaluate three ADC topologies: SAR, Delta-Sigma (\(\Delta\Sigma\)), and pipelined architectures. While conventional SAR ADCs achieve \(\upmu\)W-level power efficiency through binary-search quantization, their performance in neural applications faces three fundamental limitations: First, comparator metastability, parasitic capacitance, and capacitor mismatch restrict the ENOB to below 12 bits. Second, the D flip-flop-based logic array incurs excessive power consumption due to the cumulative dynamic switching activity and clock network loading inherent in its architecture. Third, switching noise from large capacitor arrays exacerbates signal integrity challenges in multi-channel arrays [9]. \(\Delta\Sigma\) modulators theoretically attain 16-bit resolution through oversampling, yet suffer from mW-level power dissipation due to active integrator stages, rendering them unsuitable for BCI [10]. Pipelined architectures strike favorable speed-accuracy tradeoffs, but incur significant area overhead from digital calibration circuitry [11]. This paper presents a neural signal-optimized SAR ADC with three key enhancements for BCI constraints:  
Figure 1: Bioelectric signal frequency–amplitude ranges. Figure 2: BCI system. Compared to conventional architectures, the segmented monotonic switching scheme significantly reduces the capacitor array's footprint and power dissipation while introducing redundancy to enhance tolerance to parasitic capacitance and relax performance requirements for backend comparators. 2. The circuit design primarily employs fully dynamic comparators and a dynamic logic array with enhanced latching speed to minimize total power consumption. 3.