Abstract
In silicon technology, device metrics such as power, delay, and reliability are hitting a brick wall, prompting industry to seek alternative materials, designs, and switching principles. This thesis investigates bio-inspired computing components through a case study that uses signal processing in axonal systems. A physics-based model is developed for each component of the action potential generation in an axon. The two main building blocks of an axon system consist of sequentially activated voltage-gated ion channels (the switch) and adenosine triphosphatase powered ion pumps (the battery). However, the primary aim in this investigation is not to replicate the biological details, but to replace the quantitatively accurate yet empirical operational equations with qualitative, physics-based microscopic models. The primary purposes of focusing on the physics in this study are to deconstruct biological components in terms of solid-state analogues and to understand their potential use for efficient low-power switches, even within conventional Boolean logic. Accordingly, a nano-electro-mechanical field-effect transistor, i.e., a relay, is used to model the ion channel in order to explain how these ion channels are similar to cantilevers that can switch efficiently. The model developed matches the 7.5 mV/decade sub-thermal switching of the low-power sodium channels because it captures both the phase transition from metastable state to stable state and the charge multiplication effect. The electronic ratchets act as the ion pump that uses an energy source (adenosine triphosphate hydrolysis) to power ion flow against a concentration gradient to recharge the battery. This dissertation develops an electronic analogue of a ratchet, essentially a non-equilibrium diode. Furthermore, the research demonstrates that with the ratchet, universal Boolean logic is efficiently implemented and in fact, proves analytically why the electronic ratchet is low power due to ratchet being a voltage-controlled current source. The detailed modeling of the nano-mechanical relays and the ratchets using transport theory identify and conclude the underlying physics behind these devices' power efficient operations and limitations while making connections to the individual components in the axonal networks.
