DOI: https://doi.org/10.29363/nanoge.matnec.2022.012
Publication date: 23rd February 2022
The strong increase in digital computing power in combination with the availability of large amounts of data has led to a revolution in machine learning. Computers now exhibit superhuman performance in activities such as pattern recognition and board games. However, the implementation of machine learning in digital computers is intrinsically wasteful, with energy consumption becoming prohibitively high for many applications. For that reason, people have started looking at natural information processing systems, in particular the brain, that operate much more efficiently. Whereas the brain utilizes wet, soft tissue for information processing, one could in principle exploit any material and its physical properties to solve a problem. Here we give examples of how nanomaterial networks, in particular dopant network processing units (DNPUs), can be trained using the principle of material learning to take full advantage of the computational power of matter [1].
Previously, we have shown that a disordered (or ‘designless’) network of gold nanoparticles can be electronically configured into arbitrary Boolean logic gates using artificial evolution [2]. We demonstrated that this principle is generic and can be transferred to other material systems. By exploiting the nonlinearity of variable-range hopping transport in a nanoscale network of boron dopants in silicon, we can significantly facilitate classification. Using a convolutional neural network approach, it becomes possible to use our device for handwritten digit recognition [3]. An alternative material-learning is approach is followed by first mapping our Si:B network on a deep neural network model, which allows for applying standard machine-learning techniques in finding functionality [4]. Finally, we show that the widely applied machine learning technique of gradient descent can be directly applied in materio, using the principle of homodyne gradient extraction, opening up the pathway for autonomously learning hardware systems.
We thank M. H. Siekman and J. G. M. Sanderink for technical support. We acknowledge financial support from the University of Twente, the Dutch Research Council (NWA Startimpuls grant no. 400-17-607) and the Natuurkunde Projectruimte (grant no. 680-91-114). This work was further funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through project 433682494 – SFB 1459.
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