Center for Brain-Inspired Nano Systems (BRAINS), University of Twente
Here we introduce a number of experiments towards ‘intelligent’ disordered nanomaterial systems, where we make use of “material learning” to realize functionality. By exploiting the nonlinearity of a nanoscale network of dopants in silicon, referred to as a dopant network processing unit (DNPU), we can significantly facilitate handwritten digit classification [T. Chen et al., Nature 577, 341 (2020).]. We now show that our devices are not only suitable for solving static problems but can also be applied in highly efficient real-time processing of temporal signals at room temperature. We use a dynamic DNPU circuit for time-domain feature extraction and an analogue in-memory computing chip for subsequent classification. We demonstrate software-level accuracies for a speech-recognition benchmark task (TI-46 Word) for a material-based processor.