This article introduces a successive approximation register (SAR) analog-to-digital converter (ADC) that utilizes a foreground capacitor mismatch self-calibration method. The proposed floating operation puts the uncalibrated high-bit capacitor into the floating state, preventing the sub-ADC from saturating caused by comparator static offset during the calibration process. To address the random mismatch of the LSB capacitors and improve the calibration accuracy, this article employs round-robin grouping of eight sets of LSB capacitors. In addition, a precharged bootstrapped switch is proposed to achieve high sampling linearity with low power consumption and area overhead. An anti-interference custom-designed 0.5-fF capacitor structure is suggested for binary-weighted capacitor mismatch of capacitive DAC (CDAC). Furthermore, the circuit implementation of the comparator utilized by ADC is also discussed. The prototype was fabricated in a 180-nm CMOS process with a 1.8-V supply and achieved spurious-free dynamic ranges of 108.9 and 92.38 dB at an input frequency of 1 kHz while operating at sampling rates of 100 kS/s and 1 MS/s, respectively. The prototype consumes 6.745 mW and occupies 0.91$\text {mm}^{2}$.

As current Noisy Intermediate Scale Quantum (NISQ) devices suffer from decoherence errors, any delay in the instruction execution of quantum control microarchitecture can lead to the loss of quantum information and incorrect computation results. Hence, it is crucial for the control microarchitecture to issue quantum operations to the Quantum Processing Unit (QPU) in time. As in classical microarchitecture, parallelism in quantum programs needs to be exploited for speedup. However, three challenges emerge in the quantum scenario: 1) the quantum feedback control can introduce significant pipeline stall latency; 2) timing control is required for all quantum operations; 3) QPU requires a deterministic operation supply to prevent the accumulation of quantum errors.

We propose an approach to surface reconstruction using active tactile sensing and surface normal information. Unlike previous works which consider primarily the problem of reconstructing a single tensor B-spline patch based on deformed planar or spherical domain, the surface reconstructed here is a network of triangular B-spline patches based on arbitrary topological domain and can maintain tangent plane continuity. The input is unorganized tactile sensing 3D data points with normal information of arbitrary topological type surfaces, which are obtained by a dextrous multifingered hand equipped with force/moment tactile sensors. The surface reconstructed is a parameterization of the data over these patches. An adaptive refinement is explored on the patch network in order to satisfy a given error tolerance. Demonstrations are carried out on simulated and experimental data.