Continuous kinetic model improves circuit simulation of molecular memristors
Continuous Kinetic Model of Counterion-Mediated Redox Switching in 14-Bit Molecular Memristors for Analog In-Memory Computing
Hardware Architecture
Summary
Molecular memristors are tiny devices that switch electrical signals with very high precision, useful for storing data inside computer chips. The authors found a way to describe how these devices work smoothly, avoiding jolts or breaks that made simulating whole circuits difficult before. Their new model simplifies the complex chemistry and movement of charged particles into a form that existing simulation tools can use reliably. This advance helps engineers predict how circuits with molecular memristors behave, enabling better designs for future computing hardware.
What this means in practice
- •For circuit designers: Simulate complex circuits incorporating molecular memristors without solver failures using a new continuous kinetic model compatible with industry tools.
- •For hardware architects: Predict analog memory behavior in computing hardware at high precision to design efficient in-memory computing systems with molecular components.
Authors
Logan Larsh, Dhiman Biswas, Nazmus Saadat As-Saquib, Su-in Yi, Thirumalai Venkatesan, Mike Banad
Abstract
Kinetic molecular memristors offer a compelling path toward high-density analog in-memory computing by achieving 14-bit precision, wherein discrete redox transitions and counterion displacements dictate non-volatile conductance across a deterministic energy landscape. However, integrating these nanoscale kinetics into macroscale circuit simulators has remained bottlenecked by derivative discontinuities and solver convergence failures. Here, we present a physics-inspired continuous kinetic model that bridges microscopic molecular transport with scalable circuit architectures. By casting discrete intramolecular states, counterion relaxation dynamics, and Marcus-Hush-Chidsey electron transfer rates into continuously differentiable state variables, we derive a robust, Spectre-compatible compact model and a matching vectorized Python solver. This work culminates in a unified computational platform to reliably predict the behavior of complex circuits built on these molecular memristors.