Graphene is well-suited for ultra-low-power (ULP) nano-electronics due to its exceptional characteristics like ballistic transport and its ability to engineer structures fea-turing a geometry-induced bandgap. Identifying the conditions necessary for achieving the maximum level of performance and of power-efficiency frequently requires a design space exploration (DSE). By means of calibration and external regulation of the supply voltage, the ULP graphene-nanoribbon (GNR) ring oscillator presented in this paper is capable of exceeding the performance of its 7 nm FinFET counterpart both in terms of maximum frequency and of power-efficiency. Under nominal supply voltage conditions we achieve a 1.89× higher output frequency while simultaneously reducing the power consumption by 553.8× and achieving a 812× higher power efficiency. After performing a DSE and lever-aging both externally-applied supply voltage modulation and output frequency calibration we achieved a 4.81× higher maximum output frequency operation mode and a 242× higher maximum power-efficiency operation mode when configuring both blocks for peak performance for each mode.

In the context of an artificial intelligence and machine learning landscape that is evolving at an unprecedented pace, we propose a low power, high-speed, mixed-signal graphene nanoribbon-based (GNR) McCulloch-Pitts neuron (MCPN) implementation featuring programmable synaptic weights and inhibitory inputs. By definition, a generic MCPN is comprised of two parts, a weighted summation element and a decision element, called a soma. Our summation element implementation uses three distinct non-rectangular GNR devices, biased under specific conditions, to fulfill the roles of current source, low-side and high-side switches. The programmable excitatory and inhibitory synapses were obtained leveraging GNR SRAM cells and logic gates, hence providing the flexibility needed by real-world applications. The decision element's threshold activation function was implemented using a chain of GNR inverter structures which manifest the function's characteristic in the analog domain. Modulation of the decision element's threshold is achieved indirectly by means of a configurable resistive load which is varied depending on the configuration stored in SRAM. Our benchmark results, obtained using a generic 5 by 5 pixel pattern recognition application, reveal that the GNR-based implementation achieves 3.5× less power consumption, 20 × higher speed, while occupying 3 × less active area when compared to its FinFET analog circuit counterpart.

Identifying methods to further push the boundaries of existing low-power designs has gained new traction, driven by the wide-scale use of large language models. Graphene is well-suited for ultra-low-power nano-electronics due to its exceptional characteristics like ballistic transport, flexibility, and bio-compatibility. In this paper we investigate the feasibility of using ultra-low-voltage GNR structures, in conjunction with power reduction techniques to implement a GNR-based current-starved ring oscillator. By exploiting their low operating voltage and attofarad range intrinsic capacitances we achieve a 1.89 x higher output frequency while simultaneously reducing the power consumption by 553.8 x and achieving a 812 x higher power efficiency.

McCulloch-Pitts neuron structures are comprised of a number of synaptic inputs and a decision element, called soma. In this paper, we propose a 5-bit Graphene Nanoribbon (GNR)-based DAC to fulfill the role of the summation element featuring programmable input weights. The proposed GNR-based 5-bit DAC relies on: (i) GNR unit current cells and (ii) a GNR logic thermometric decoding block. Our implementation is based on mapping the GNR structure's conductance using Matlab and performing the required SPICE analysis using the Matlab based GNR Verilog-A model. The unit current cell geometry and bias conditions were chosen based on the unit cell's conductance map from which we derived its I ON/IOFF ratio, as well as transfer and output characteristics, resembling the classical MOSFET counterpart. By utilizing GNR devices instead of FinFET counterparts, a reduction of the active area of the 5-bit current DAC by up to a factor of three can be achieved. Furthermore, the GNR implementation achieved this while maintaining comparable INL and DNL performance to that of the FinFET variant, i.e., DNL of [-0.196, 0.088] LSB and INL of [-0.809, 0.364] LSB for the proposed GNR 5-bit DAC while operating at a supply voltage of only 0.2 V.