Reconfigurable MoS₂ Memtransistors for Continuous Learning in Spiking Neural Networks

Continued miniaturization in digital electronics over the past several decades has led to modern ubiquitous technologies such as the Internet of Things, edge computing, artificial intelligence (AI), and machine learning (ML) that are impacting nearly all aspects of society. However, current AI/ML algorithms incur undesirable energy costs on conventional von Neumann hardware platforms, motivating exploration of more efficient neuromorphic architectures exemplified by the human brain. Compared to previous two-terminal neuromorphic architectures such as memristors and phase change memory, three-terminal memtransistors have emerged as a tunable biorealistic neuromorphic system due to co-location of a field-effect transistor and non-volatile memory within one device. By integrating atomically thin two-dimensional materials that enable enhanced electrostatic tunability, monolayer polycrystalline MoS₂ memtransistors achieve gate-tunable memristive switching, linearity, and reconfigurability [1]. Similarly, four-terminal dual-gated MoS₂ memtransistors minimize crosstalk and sneak currents in scalable crossbar architectures, simplifying integration challenges that have hindered memristive architectures based on bulk materials [2]. Despite the unique attributes of memtransistors, their implementation in neuromorphic architectures has been limited to conventional artificial neural networks [3] suggesting that their full potential for AI/ML has not yet been realized.<br/><br/>This presentation will introduce MoS₂ memtransistors with a wide range of learning behaviors achieved through a combination of enhanced electrostatic control and tailored gate bias pulsing profiles. Using monolayer MoS₂ grown on sapphire, long-term potentiation and depression behaviors are singularly modulated by the gate electrode polarity, an observation that parallels the synaptic weight update and neuroplasticity in biological systems [4]. By enhancing the relative importance of the vertical field effect from the gate voltage compared to the lateral field from the drain voltage, these devices show a greater reconfigurability of synaptic behavior compared to previously reported MoS₂ memtransistors grown on SiO₂. Different gate bias pulsing strategies further diversify the library of learning curves. The resulting gate-tunable learning behavior is then modeled in a simplified spike-timing-dependent plasticity (STDP) scheme to perform unsupervised continuous learning in a simulated spiking neural network (SNN). We show that continuous learning, a previously underexplored cognitive concept in hardware neuromorphic computing, circumvents traditional trade-offs between image recognition accuracy and resource allocation. Overall, this work demonstrates that reconfigurable MoS₂ memtransistors provide unique hardware accelerator opportunities for energy-efficient artificial intelligence.<br/><b>References</b><br/>[1] V. K. Sangwan, H.-S. Lee, H. Bergeron, I. Balla, M. E. Beck, K.-S. Chen, and M. C. Hersam, <i>Nature</i>, <b>554</b>, 500-504 (2018)<br/>[2] H.-S. Lee, V. K. Sangwan, H. Bergeron, H. Y. Jeong, K. Su, and M. C. Hersam, <i>Adv. Funct. Mater.,</i> <b>30,</b> 2003683 (2020).<br/>[3] X. Feng; S. Li; S. L. Wong.; S. Tong; L. Chen; P. Zhang; L. Wang; X. Fong; D. Chi; K.-W. Ang, <i>ACS Nano</i>, <b>15,</b> 1764-1774 (2021).<br/>[4] J. Yuan, S. E. Liu, A. Shylendra, W. A. Gaviria Rojas, S. Guo, H. Bergeron, S. Li, H.-S. Lee, S. Nasrin, V. K. Sangwan, A. R. Trivedi, and M. C. Hersam, <i>Nano Letters</i>, <b>21</b>, 6432-6440 (2021).