Design and Characterization of an Asynchronous Fixed Priority Tree Arbiter for SPAD Array Readout

The usage of single-photon avalanche diode arrays is becoming increasingly common in various domains such as medical imaging, automotive vision systems, and optical communications. Nowadays, thanks to the development of microelectronics technologies, the SPAD arrays designed for these applications has been drastically well-facilitated, allowing for the manufacturing of large matrices. However, there are growing challenges for the design of readout circuits with the needs of reducing their energy consumption (linked to the usage cost) and data rate. Indeed, the design of the readout circuit for the SPAD array is generally based on synchronous logic; the latter requires synchronization that may increase the dead time of the SPADs and clock trees management that are known to increase power consumption. With these limitations, the long-neglected asynchronous (clockless) logic proved to be a better alternative because of its ability to operate without a clock. In this paper, we presented the design of a 16-to-1 fixed-priority tree arbiter readout circuit for a SPAD array based on asynchronous logic principles. The design of this circuit was explained in detail and supported by simulation results. The manufactured chip was tested, and the experimental results showed that it is possible to record up to 333 million events per second; no reading errors were detected during the data extraction test.

microFE-BCI: An Asynchronous Practical EEG-based Control Paradigm Assisted by Micro-facial-expressions

<div><p>In this work, an EEG-based control paradigm assisted by micro-facial-expressions (microFE-BCI) was developed, focusing on the mainstream defect as the insufficiency of real-time capability, asynchronous logic, and robustness. The core algorithm in microFE-BCI contained two stages (asynchronous ‘ON’ detection & microFE-BCI based real-time control) with four steps (obvious non-microFE-EEGs exclusion, interface ‘ON’ detection, microFE-EEGs real-time decoding, and validity judgment). It provided the asynchrounous function, decoded 8 instructions from the latest 100 ms EEGs, and greatly reduced the frequent misoperation. In the offline assessment, microFE-BCI achieved 96.46%±1.07 accuracy for interface ‘ON' detection and 92.68%±1.21 for microFE-EEGs real-time decoding, with the theoretical output timespan less than 200 ms. This microFE-BCI was implemented into a software, and applied to two online manipulations for evaluating the stability and agility. In object-moving with a robotic arm, the averaged IoU was 60.03±11.53%. In water-pouring with a prosthetic Hand, the averaged water volume was 202.5±7.0 ml. During online, microFE-BCI performed no significant difference (P = 0.6521 & P = 0.7931) with commercial control methods (i.e., FlexPendant and Joystick), indicating a similar level of controllability and agility. This study demonstrated the capability of microFE-BCI, enabling a novel solution to the noninvasive BCIs in real-world challenges.</p></div>

microFE-BCI: An Asynchronous Practical EEG-based Control Paradigm Assisted by Micro-facial-expressions

<div><p>In this work, an EEG-based control paradigm assisted by micro-facial-expressions (microFE-BCI) was developed, focusing on the mainstream defect as the insufficiency of real-time capability, asynchronous logic, and robustness. The core algorithm in microFE-BCI contained two stages (asynchronous ‘ON’ detection & microFE-BCI based real-time control) with four steps (obvious non-microFE-EEGs exclusion, interface ‘ON’ detection, microFE-EEGs real-time decoding, and validity judgment). It provided the asynchrounous function, decoded 8 instructions from the latest 100 ms EEGs, and greatly reduced the frequent misoperation. In the offline assessment, microFE-BCI achieved 96.46%±1.07 accuracy for interface ‘ON' detection and 92.68%±1.21 for microFE-EEGs real-time decoding, with the theoretical output timespan less than 200 ms. This microFE-BCI was implemented into a software, and applied to two online manipulations for evaluating the stability and agility. In object-moving with a robotic arm, the averaged IoU was 60.03±11.53%. In water-pouring with a prosthetic Hand, the averaged water volume was 202.5±7.0 ml. During online, microFE-BCI performed no significant difference (P = 0.6521 & P = 0.7931) with commercial control methods (i.e., FlexPendant and Joystick), indicating a similar level of controllability and agility. This study demonstrated the capability of microFE-BCI, enabling a novel solution to the noninvasive BCIs in real-world challenges.</p></div>

A Noise Reduction 12-bit 125-MSPS SAR ADC with Modified Asynchronous Logic Regulation Technique

This paper presents noise reduction and modified asynchronous logic regulation techniques used in successive approximation register (SAR) analog-to-digital converter (ADC). With a transconductance enhanced structure, noise reduction is provided in the dynamic comparator. The input referred noise of the proposed comparator is about 165[Formula: see text][Formula: see text]V rms at 60∘C (typical corner). An enhanced-positive-feedback loop is introduced to reduce the regeneration delay of the comparator. In addition, a modified asynchronous logic regulation technique is exhibited, a clock with adaptable delay is driving the comparator in approximation phase. Consequently, the settling accuracy of DAC (Digital-to-Analog Converter) is enough and the conversion speed of SAR ADC is increased without any redundant cycles. To demonstrate the proposed techniques, a design of SAR ADC is fabricated in 65-nm CMOS technology, consuming 4[Formula: see text]mW from 1.2[Formula: see text]V power supply with a [Formula: see text][Formula: see text]dB and [Formula: see text][Formula: see text]dB. The proposed ADC core occupies an active area of 0.048[Formula: see text]mm2, and the corresponding FoM is 27.2[Formula: see text]fJ/conversion-step at Nyquist rate.