Molecular Building Blocks for Artificial Intelligence

Molecular electronic switches have been a research topic for about three decades. The first wave in the nineties revolved around the appealing concept that molecules might make controllable nano-scale switches by self-assembly. However, the molecules proved to be fragile, and their switching endurance was far too low to be useful. Recently, molecular memristive circuit elements based on redox-active transition metal complexes of azo aromatic ligands have demonstrated resistive switching performance superior to several inorganic oxides, which calls for a serious examination of their chemical and physical properties and potential applications. Beyond being a simple on-off switch or binary storage element, molecular memristors offer several unique features: deterministic (as opposed to stochastic) and uniform (as opposed to filamentary) resistance switching, multiple resistance levels, simultaneous memristance and memcapacitance, and multiple serial non-monotonic switching events. Can these characteristics offer a significant benefit to computing performance? In this presentation, I will introduce a new generation of molecular circuit elements designed to capture intricate, reconfigurable, dynamic logic within nano-scale material properties. These devices, teetering on the edge of instability, hold promise for emulating brain functions. Our exploration spans from fundamental device principles to the investigation of circuits and on-chip integration [1-6], aiming to establish promising platforms for artificial intelligence and machine learning in the post-Moore era.<br/><b>References: </b><br/>[1] Sreebrata Goswami, Williams, R. Stanley, and Sreetosh Goswami. "Potential and challenges of computing with molecular materials." Nature Materials (2024): 1-11.<br/>[2] Yi, S. I., Rath, S. P., Deepak, Goswami, S., Williams, R. S., & Goswami, S. (2022). Energy and Space Efficient Parallel Adder Using Molecular Memristors. Advanced Materials, 2206128.<br/>[3] Goswami, Sreetosh, et al. "Decision trees within a molecular memristor." Nature 597.7874 (2021): 51-56.<br/>[4] Goswami, Sreetosh, et al. "Robust resistive memory devices using solution-processable metal-coordinated azo aromatics." Nature Materials 16.12 (2017): 1216- 1224.<br/>[5] Goswami, Sreetosh, et al. "Charge disproportionate molecular redox for discrete memristive and memcapacitive switching." Nature Nanotechnology 15.5 (2020): 380- 389.<br/>[6] Rath, Santi Prasad, Thompson, Damien, Goswami, Sreebrata, & Goswami, Sreetosh. "Many body molecular interactions in a memristor." Advanced Materials (2022): 2204551.