DIDACTIC: A Data-Intelligent Digital-to-Analog Converter with a Trainable Integrated Circuit using Memristors

In an increasingly data-diverse world, in which data are interactively transferred at high rates, there is an ever-growing demand for high-precision data converters. In this paper, we propose a novel digital-to-analog converter (DAC) configuration that is calibrated using an artificial intelligence neural network technique. The proposed technique is demonstrated on an adaptive and self-calibrated binary-weighted DAC that can be configured on-chip in real time. We design a reconfigurable 4-bit DAC with a memristor-based neural network. This circuit uses an online supervised machine learning algorithm called 'binary-weighted time-varying gradient descent.' This algorithm fits multiple full-scale voltage ranges and sampling frequencies by iterative synaptic adjustments, while inherently providing mismatch calibration and noise tolerance. Theoretical analysis, as well as simulation results, show the efficiency and robustness of the training algorithm in reconfiguration, self-calibration, and desensitization, leading to a significant improvement in DAC accuracy: 0.12 LSB in terms of integral non-linearity, 0.11 LSB in terms of differential non-linearity, and 3.63 bits in terms of effective number of bits. The findings constitute a promising milestone toward scalable data-driven converters using deep neural networks.