A Mixed-Mode Simulation Framework to Investigate Neuronal Signal Transduction and Sensing at Nanoelectrodes Federico Leva1*, Pierpaolo Palestri2, Luca Selmi1 1Dipartimento di Ingegneria ‘‘Enzo Ferrari’’, Università di Modena e Reggio Emilia, Modena, Italy 2Polytechnic department of Engineering and Architecture University of Udine, Udine, Italy *Corresponding author: federico.leva@unimore.it Overview Figure 1: Schematic representation of an intracellular (a) and extracellular (b) FET-based nanoelectrode integrated in CMOS technology, interfaced with a neuron and connected to a transimpedance readout circuit. Accurate physics-based numerical models of coupled neuron-sensor-readout CMOS-based systems are useful to: • design and optimize the sensing device and the readout, • interpret the measurements, • derive lumped-element descriptions of the recording systems related to geometrical and physical properties. A semiconductor-TCAD-based mixed-mode modeling environment is suited for such purposes. We exemplify its capabilities by analyzing relevant figures of merit (including SNR) for an active FET-based nanoelectrode envisioned for intra- and extracellular coupling to neurons (Fig. 1). This work has been financially supported by the European Union’s Horizon 2020 project “IN-FET” (Grant Agreement n. 862882) via the IUNET consortium. Methods The modelling framework consists of a FEM TCAD for semiconductors [1], which supports: • Mixed device-circuit simulations with user-defined physics and lumped-elements via PMI and CMI, respectively [1]. • Drift-diffusion transport in the intra- and extracellular electrolyte regions, implemented by mimicking anions and cations with electrons and holes, and suitably adjusting the semiconductor properties [2,3,4]. • Combined DC steady state, transient, AC small signal, and noise analysis. • Cellular membrane compartment description as (i) lossless insulator in TCAD (with parameters consistent to [5]), or (ii) lumped capacitor in the circuit portion. • Action potentials (APs) incorporation onto each membrane’s compartment as either: (i) voltage generator (Fig. 2.b), (ii) current generator (Fig. 2.c), or (iii) Hodgkin- Huxley (HH) [5] circuit model (Fig. 2.a), implemented via custom multi-port memristors described by CMIs. The CMIs set the (de)activation state-variables for the non- linear conductances of the K+ and Na+ ion-channels [5]. Results • Fig. 3 shows that our mixed-mode approach predicts the expected monophasic (a) and biphasic (b) waveforms of lumped-element circuit models (not shown) derived from state-of-art results [6] for intra- and extracellular contact. • Fig. 4 reports the neuron-to-readout transfer function (TF1) computed with AC small signal analysis. A band-pass behavior is observed for intracellular contact, whereas a derivative behavior for the extracellular case on the frequency range of the AP spectrum. The TF shapes are consistent with the time-domain profiles of Fig. 3. • Fig. 5 shows that the active FET-sensor SNR2 is not affected by the presence of the readout noise and only slightly affected by the coupling condition with the neuron, overall achieving adequately large values. • Extended results can be found in [7]. Figure 2: Different equivalent circuits to include the generation of action potentials across the passive neuron membrane: (a) Hodgkin-Huxley model; (b) external voltage boundary; (c) external current boundary. Figure 3: AP waveform comparison for TCAD and full L.E. Circuit descriptions of an active nanoelectrode in (a) intracellular and (b) extracellular contact (Fig. 1). Vm is the intracellular neuron signal, Vout is the output signal of the readout. Figure 4: Modulus of the system transfer function of the active intracellular and extracellular sensing systems (Fig. 1). Figure 5: SNR for the different access types, and in presence or absence of the readout noise. The readout gain has been set to provide recorded signals with 1 Vpp. References [1] Synopsys, I