The tunability of a physical reservoir computing device is a challenge and difficult to rectify. This is mainly due to the complexity of the charge transfer mechanism in nanostructures used to make a microdevice. The randomness of the nanomaterial network is suggested to improve the physical reservoir computing performance, but such randomness makes it even more challenging to identify the source of performance improvement or decline.  During this year, we aimed to fabricate a tunable 3D reservoir computing device using SWCNT-PVA nanocomposite via manipulation of junction resistance. We were able to fabricate a 3D porous network of SWCNT-PVA nanocomposite successfully. However, an issue arose during the measurement of reservoir computing performance due to the low mechanical stability of the nanocomposite. The nanocomposite was soft and deformable and unable to recover its original state, making it challenging to perform a stable and reliable reservoir computing performance assessment. In addition, the high-water susceptibility of PVA made it difficult to perform further analysis on the nanocomposite. Although I do believe there are other applications for SWCNT-PVA porous nanocomposite in the current state, it is not suitable as a physical reservoir computing device. The current issue could possibly be rectified by introducing additives to improve the mechanical stability of the nanocomposite. So far, the reservoir computing performance of SWCNT-PVA nanocomposite results have been inconclusive, but I am in the process of improving the structural stability.

My research on physical reservoir computing did not stop due to the challenges I faced. While working on SWCNT-PVA, as a result of my collaboration with Professor Hirofumi Tanaka of the Kyushu Institute of Technology, we were able to publish an article in ACS Applied Electronic Materials with the title “High Performance of an In-Material Reservoir Computing Device Achieved by Complex Dynamics in a Nanoparticle Random Network Memristor.”