Abstract: Conducting Polymer Dendrites (CPD) can engrave sophisticated patterns of electrical interconnects in their morphology, networking input with output nodes, from low-voltage spikes and with very minimal amounts of resources: they may unlock in operando man ufacturing functionalities for an electronics framework using metamorphism conjointly with electron transport as part of the information processing. The relationship between their structure and the information transport is still however very unclear and hinders the exploitation of the versatility of their morphologies to store and process electrodynamic information. This study details the evolution of CPD's circuit parameters with their growth and shape. By the means of electrochemical impedance spectro scopy (EIS), multiple distributions of relaxation times (DRT) are evidenced and evolve specifically upon growth. Correlations are established between the dispersive capacitance of dendritic morphologies and their growth duration, independently from exoge nous physical variables, such as distance, multi-component evaporation or aging. Deviation of the anomalous capacitance from the conventional Debye dielectric relaxation can be programmed within the morphology, as the growth controls the dispersion coeff icient of the dendrite's constant-phase elements relaxation. These results suggest that the fading-memory time window of pseudo-capacitive interconnects can practically be conditioned using electrogenerated CPD morphogenesis as an in materio learning mec hanism. This study confirms the perspective of using electrochemistry for unconventional electronics, engraving information with low voltage events in the physics of conducting polymer objects, and storing information in their morphology, accessible by i mpedance spectral analysis.

Abstract: Contributions of organic semiconducting materials to electronics are particularly hard to assess: As macromolecular organizations, they have low enthalpy so they can be processed in soft conditions and they have resilience to deformation. However, for th e same reason, they have also broader density of energy states and more instabilities than silicon in ambient. Controlling matter's order at low scale and its properties for as long as possible were always golden standards for microelectronics. Neverthel ess, in a time where brain functioning rises even more as a source of inspiration, shall it still be so? Here are presented clues on how physical property dispersions may be relevant features for information generator nodes to recognize patterns. In a co ntext where the information to recognize is not trivial to physically define, no model can rule sensors' classification a priori. Despite this, broadening the conducting polymer temporal responses in a sensing array allows recognizing dynamical voltage p atterns, or broadening conducting polymer's chemistry in a sensing array enlarges a classifier's perception field to recognize solvent vapors in air. By the nature of property dispersions in regards to the information to recognize, physical variabilities (structural and chemical) can be assets to exploit for pattern recognition and not necessarily drawbacks to bypass for hardware manufacturing. The brain architecture is also transient: a part of the processed information is engraved in its topology, sho wing that a hardware classifier can make use of physical instabilities as part of its programing, by forming new connections in a nodal architecture. Some evidences are also presented here, on how dendritic morphogenesis of a conducting polymer can be a mean to store past voltage experiences in the impedance between nodes in a topology. Very distinct electrochemical features appear in the readout impedance information after growth and these features are to be associated with the shape of a voltage wave inputted on the junction. By the physical implementation of materials' disorder and transience in electronics devices, it is expected that organic semiconductors will integrate essential ingredients in future-emerging information generator nodes beyond s ensors: from embedded random information generating resources to evolving abilities in information classification architectures.

Abstract: In electronics, circuits are predefined for a lifetime. Fabricating always the same with highly stable components is a real advantage for mass production. However when technologies are no longer up to date, electronic devices can only be discarded, poorl y recycled, because they cannot adapt. Living organisms, on the other hand, are constantly evolving. They learn and interact with their environment, and when doing so, brain-neurons or tree-roots grow with appearent disorder but a surprising way to learn to exploit local ressources. The way they branch out can be described as morphogenesis and is the symbol of a natural intelligence, too complex to modelize. While conventional electronics seeks to eliminate disorder and variability, we hypothesize that it is possible to make use of it in a novel electronics that uses electrochemistry to mimic biological processes for adaptation. In this study, we will discuss on morphogenesis of a conducting polymer (PEDOT:PSS) through the AC electropolymerization of E DOT in water. The dendritic objects exhibit various morphologies, differing in their thicknesses and number of branches. Their growth mechanism involves diffusion and electromigration of charged species within the solution. We also present the first resu lts characterizing a connection of those objects in the frequency domain, where various dynamics can be observed due to specific mechanisms at the different interfaces. The electropolymerization of EDOT offers an inexpensive way to grow directed connecti ons with a specific impedance to connect components in a system by voltage activation. It could be used to address the limits of the current electronics in terms of cost and flexibility while taking a form that is closer to what can be found in nature.