A Machine Learning Approach to Support Neuromorphic Device Design and Microfabrication.

Neuromorphic chips provide a potential solution for sustainable computing, as they attempt to mimic the neu-ronal architectures in human brain and show great potentials in reducing energy consumption in the order of magnitude and also improve the computational performance. However, the fabrication process for neuromophic chips is costly and currently based on trial-and-error, which adds complexity to the design process. In this paper, we address these challenges by designing and developing machine learning guided microfabrication process for Resistive Random Access Memory (RRAM), which is a key device in neuromorphic chips. Specifically, our research makes the following contributions: 1) we successfully fabricated a new RRAM using bio-organic materials, leading to a greener and more sustainable solution for supporting neuromorphic computing; 2) we carried out a comprehensive study on the microfabrication process conditions and their effects on the RRAM devices, producing new knowledge to the field; 3) we developed a synthetic data assisted approach to predict key performance metrics of the bio-organic RRAM (bio-RRAM) devices, without requiring substantial amount of experimental training data; and 4) we developed a more advanced approach which leverages learning task conversion to make predictions on fine-grained performance metrics with no added requirements for experimental or synthetic data. We evaluated these approaches using eight honey-based bio-RRAM devices we fabricated, and the results show that both approaches are effective in terms of predicting the devices performance. We expect that the machine learning guided microfabrication will pave the way to more efficient and effective design of the next-generation of RRAM devices for green neuromorphic computing.