Analog Programing of Conducting-Polymer Dendritic Interconnections and Control of their Morphology

TL;DR

This paper demonstrates how to emulate dendritic morphogenesis in conducting polymers through electropolymerization, enabling in operando material modification for hardware learning and neuromorphic applications.

Contribution

It introduces a method to control dendritic morphology via voltage pulses during electropolymerization, mimicking biological neural growth processes.

Findings

Morphological features can be tuned independently using voltage-pulse parameters.

Dendritic growth dynamics are influenced by input signal frequency and duty cycle.

Controlled dendritic structures enhance neuromorphic hardware performance.

Abstract

Although materials and processes are different from biological cells', brain mimicries led to tremendous achievements in massively parallel information processing via neuromorphic engineering. Inexistent in electronics, we describe how to emulate dendritic morphogenesis by electropolymerization in water, aiming in operando material modification for hardware learning. The systematic study of applied voltage-pulse parameters details on tuning independently morphological aspects of micrometric dendrites': as fractal number, branching degree, asymmetry, density or length. Time-lapse image processing of their growth shows the spatial features to be dynamically-dependent and expand distinctively before and after forming a conductive bridging of two electrochemically grown dendrites. Circuit-element analysis and electrochemical impedance spectroscopy confirms their morphological control to occur in temporal windows where the growth kinetics can be finely perturbed by the input signal frequency and duty cycle. By the emulation of one of the most preponderant mechanisms responsible for brain's long-term memory, its implementation in the vicinity of sensing arrays, neural probes or biochips shall greatly optimize computational costs and recognition performances required to classify high-dimensional patterns from complex aqueous environments.