Hamam Abd, Andreas König

• The number of sensors used in modern devices is rapidly increasing, and the interaction with sensors demands analog-to-digital data conversion (ADC). A conventional ADC in leading-edge technologies faces many issues due to signal swings, manufacturing deviations, noise, etc. Designers of ADCs are moving to the time domain and digital designs techniques to deal with these issues. This work pursues a novel self-adaptive spiking neural ADC (SN-ADC) design with promising features, e.g., technology scaling issues, low-voltage operation, low power, and noise-robust conditioning. The SN-ADC uses spike time to carry the information. Therefore, it can be effectively translated to aggressive new technologies to implement reliable advanced sensory electronic systems. The SN-ADC supports self-x (self-calibration, self-optimization, and self-healing) and machine learning required for the internet of things (IoT) and Industry 4.0. We have designed the main part of SN-ADC, which is an adaptive spike-to-digital converter (ASDC). The ASDC is based on a self-adaptive complementary metal–oxide–semiconductor (CMOS) memristor. It mimics the functionality of biological synapses, long-term plasticity, and short-term plasticity. The key advantage of our design is the entirely local unsupervised adaptation scheme. The adaptation scheme consists of two hierarchical layers; the first layer is self-adapted, and the second layer is manually treated in this work. In our previous work, the adaptation process is based on 96 variables. Therefore, it requires considerable adaptation time to correct the synapses’ weight. This paper proposes a novel self-adaptive scheme to reduce the number of variables to only four and has better adaptation capability with less delay time than our previous implementation. The maximum adaptation times of our previous work and this work are 15 h and 27 min vs. 1 min and 47.3 s. The current winner-take-all (WTA) circuits have issues, a high-cost design, and no identifying the close spikes. Therefore, a novel WTA circuit with memory is proposed. It used 352 transistors for 16 inputs and can process spikes with a minimum time difference of 3 ns. The ASDC has been tested under static and dynamic variations. The nominal values of the SN-ADC parameters’ number of missing codes (NOMCs), integral non-linearity (INL), and differential non-linearity (DNL) are no missing code, 0.4 and 0.22 LSB, respectively, where LSB stands for the least significant bit. However, these values are degraded due to the dynamic and static deviation with maximum simulated change equal to 0.88 and 4 LSB and 6 codes for DNL, INL, and NOMC, respectively. The adaptation resets the SN-ADC parameters to the nominal values. The proposed ASDC is designed using X-FAB 0.35 µm CMOS technology and Cadence tools.