Towards Bio-inspired Computing using 2D Materials based Transistors
Open Access
- Author:
- Subbulakshmi Radhakrishnan, Shiva
- Graduate Program:
- Engineering Science and Mechanics
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- August 01, 2024
- Committee Members:
- Laura Cabrera, Program Head/Chair
Abhronil Sengupta, Outside Unit & Field Member
Jian hsu, Major Field Member
Patrick Lenahan, Major Field Member
Saptarshi Das, Chair & Dissertation Advisor - Keywords:
- 2D Materials
Field-Effect Transistors
Biomimetic
Spiking Neural Network
Artificial Intelegence
Sensing
Computing - Abstract:
- The Internet of Things (IoT) is experiencing rapid and accelerating expansion, with edge devices producing staggering amounts of data at an exponential rate. This surge necessitates the development of more efficient and robust computing architectures to handle the immense data flow, as traditional cloud-based computations introduce latency and security vulnerabilities. Artificial neural networks (ANNs) are increasingly required to process these vast amounts of data and extract meaningful insights. However, despite their advancements and contributions to modern computing, ANNs still fall short compared to biological neural networks (BNNs) in terms of energy efficiency, multifunctionality, and adaptability. Remarkably, the human brain performs complex computations while consuming a mere 20W of power, thus inspiring the field of neuromorphic computing. This thesis explores the frontier of neuromorphic computing, focusing on BNNs, the next generation of ANNs capable of reproducing neuronal temporal dynamics with high fidelity. We present three innovative methodologies for the hardware implementation of BNNs, leveraging the potential of two-dimensional (2D) materials, particularly transition-metal dichalcogenides (TMDCs) such as MoS2, to achieve multifunctional field-effect transistors (FETs) and realize energy-efficient computation. Using a commercial silicon photodiode for sensing and introducing a biomimetic dual-gated MoS2 FET that transforms the sensed analog input into stochastic spike trains, we demonstrate three distinct encoding methods: rate-based, spike timing, and spike count-based. This approach consumed just 1-5 pJ per spike, which is remarkable. When the encoded spikes was fed to an SNN trained on the MNIST dataset classification task, we observed an accuracy of ~91%. Next, we built a medium-scale integrated circuit comprising 21 photosensitive 2D monolayer MoS2 Memtransistor. The circuit incorporates two consecutive three-stage inverters and an XOR logic gate, capable of sensing input light stimulus and encoding it into spike time-based signals. This setup mimics retinal ganglion cells by encoding light intensities through sporadic bursts of activity, with the time-to-first spike indicating illumination levels. Governed by non-volatile memory and analog programmability, the photo encoder exhibited adaptive spiking behavior, consuming energy in the order of microjoules for the entire process. Learning from the first two approaches, we developed a BNN that integrates multiple pixels and enhances energy efficiency. This was realized through MoS2-based optoelectronic, computational, and programmable FETs. This network emulates crucial brain operations, including sensing, encoding, learning, forgetting, and inference. The BNN demonstrated both long-term potentiation (LTP) and long-term depression (LTD), mimicking synaptic plasticity seen in biological systems. These approaches display considerable improvements in energy efficiency, scalability, and flexibility compared to traditional silicon-based neuromorphic counterparts. Leveraging 2D materials and integrated circuits for in-memory sensing and computing can help overcome the von Neumann bottlenecks. Our approach, while accurately emulating neuronal biological functions, enables advanced AI capabilities in resource-limited edge devices. This thesis lays a cornerstone for future advancements in intelligent and adaptive electronic systems, poised to cater to the ever-growing demands of the IoT era. Our work opens the doors to revolutionary, energy-efficient, neuromorphic computer architecture.