Proceedings of International Conference on Hybrid and Organic Photovoltaics (HOPV24)
DOI: https://doi.org/10.29363/nanoge.hopv.2024.155
Publication date: 6th February 2024
Neuromorphic computation is a promising way to overcome the limits of the von Neumann chip architecture in modern computing. Memristors can play a key role in the realization of neuromorphic devices due to their inherent memory storage and ability to dynamically alter physical properties such as resistance in response to an incoming stimulus.1 Organic memristive devices have the potential to generate new paradigms in the memristor device field due to the inherent ability to tune the electronic properties of organic molecules for greater electrical or memory/volatility performance, which is currently a bottleneck for solid-state transition metal oxide (TMO) memristors.2,3 We have recently developed a new approach to fabricate high performance memristive devices based 2,4-bis[4-(diethylamino)-2-hydroxyphenyl]squaraine (SQ) nanowires (NWs).4 In our initial study, SQ nanowire memristors were fabricated using a very simple but effective approach whereby SQ NWs were grown on an interdigitated gold electrode to form a lateral Au/SQ/Au architecture. These prototype devices demonstrate the hallmarks of memristor operation such as current hysteresis loops and dynamic conductivity in response to multiple voltage sweeps. The volatility of the conductivity states written to the device is also shown to have long memory retention without voltage bias, demonstrating non-volatility. These results are on par with benchmark transition metal oxide devices (TMO). These results demonstrate a straightforward and very promising approach to fabricate robust and low-cost memristive devices. Furthermore, the fabrication method offers an excellent platform for device prototyping and high-throughput screening of potential memristive materials. More recently, advanced neuromorphic tests have been applied and devices demonstrate key neuromorphic properties, including state retention, cyclability, potentiation, depression, pulsed paired facilitation and Hebbian learning. In addition to our NW studies, a brief update on our progress towards more scalable thin-film based (opto)electronic memristors will be presented.
REFERENCES:
- Zidan, M. A.; Strachan, J. P.; Lu, W. D., Nature Electron., 1 (1), 22 (2018)
- van de Burgt, Y.; Melianas, A.; Keene, S. T.; Malliaras, G.; Salleo, A, Nature Electron., 1 (7), 386 (2018)
- Sangwan, V. K.; Hersam, M. C., Nat. Nanotechnol., 15 (7), 517 (2020)
- O’Kelly, C. J., Nakayama, T. & Ryan, J. W., ACS Appl. Electron. Mater., 2, 3088 (2020)