Fernando Cardes Garcia

Last Name

Cardes Garcia

First Name

Fernando

ORCID

0000-0003-1880-6734

Organisational unit

03684 - Hierlemann, Andreas / Hierlemann, Andreas

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Publications1 - 10 of 24

Advances in large-scale electrophysiology with high-density microelectrode arrays
Schroter, Manuel; Cardes Garcia, Fernando; Bui, Cat-Vu Huu; et al. (2025)
Lab on a Chip
A detailed functional characterization of electrogenic cells, such as neurons and cardiomyocytes, by means of high-density microelectrode arrays (HD-MEAs) has emerged as a powerful approach for inferring cellular phenotypes and elucidating fundamental mechanisms underlying cellular function. HD-MEAs have been applied across a range of disciplines, including neurodevelopmental research, stem cell biology, and pharmacology, and more recently in interdisciplinary work at the intersection of biomedical engineering, computer science, and artificial intelligence (AI). Innovations in chip design, fabrication, recording capabilities, and data processing have significantly advanced the functionality of HD-MEAs. Today's chips allow the study of cellular function across scales and at high throughput. They enable the analysis of multi-parametric functional phenotypes over extended time and facilitate monitoring the effects of targeted perturbations on cellular behavior. In this Tutorial Review, we will first survey the advances in HD-MEA design and their readout and stimulation capabilities. We will then abstract studies that used HD-MEAs in combination with other experimental techniques to probe biologically relevant cellular and subcellular features, with an emphasis on in vitro applications of HD-MEAs. Thereafter, we will cover analytical techniques that are essential for analyzing and characterizing HD-MEA data. Finally, we will address current limitations of HD-MEAs and discuss potential future developments.
A miniaturized anchoring device for acute brain slice recordings with high-density microelectrode arrays
Jüngling, Fabian; Scherer, Dominik; Saseendran Kumar, Sreedhar; et al. (2025)
MEA 2025 : 13th Meeting on Neural and Electrogenic Cell Interfacing
A low-cost testbed for neural microelectrodes
Bui, Cat-Vu Huu; Maliakal, Neethu; Ulusan, Hasan; et al. (2024)
Proceedings ~ Eurosensors 2023
The performances of microelectrode arrays for neural interfaces strongly depend on electrode design. Due to a lack of simulation tools, electrode engineers often have to refine new designs empirically. This process requires setups of electrical and electrophysiological hardware that are not specific to electrode testing and unnecessarily costly. We propose a low-cost testbed for specifically targeting metrics relevant to electrode performance and functions, which relies on an off-the-shelf measurement tool and only on components necessary for such testing. We experimentally demonstrate the platform by characterizing microelectrodes by means of impedance spectroscopy and recording the extracellular action potentials from in vitro primary rat neurons.
A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors
Cartiglia, Matteo; Costa, Filippo; Narayanan, Shyam; et al. (2024)
Nature Communications
Bio-signal sensing is pivotal in medical bioelectronics. Traditional methods focus on high sampling rates, leading to large amounts of irrelevant data and high energy consumption. We introduce a self-clocked microelectrode array (MEA) that digitizes bio-signals at the pixel level by encoding changes as asynchronous digital address-events only when they exceed a threshold, significantly reducing off-chip data transmission. This novel MEA comprises a 64 × 64 electrode array, an asynchronous 2D-arbiter, and an Address-Event Representation (AER) communication block. Each pixel operates autonomously, monitoring voltage fluctuations from cellular activity and producing digital pulses for significant changes. Positive and negative signal changes are encoded as “up” and “down” events and are routed off-chip via the asynchronous arbiter. We present results from chip characterization and experimental measurements using electrogenic cells. Additionally, we interface the MEA to a mixed-signal neuromorphic processor, demonstrating a prototype for end-to-end event-based bio-signal sensing and processing.
A low‐power opamp‐less second‐order delta‐sigma modulator for bioelectrical signals in 0.18 μm cmos
Cardes Garcia, Fernando; Baladari, Nikhita; Lee, Jihyun; et al. (2021)
Sensors
This article reports on a compact and low‐power CMOS readout circuit for bioelectrical signals based on a second‐order delta‐sigma modulator. The converter uses a voltage‐controlled, oscillator‐based quantizer, achieving second‐order noise shaping with a single opamp‐less integrator and minimal analog circuitry. A prototype has been implemented using 0.18 μm CMOS technology and includes two different variants of the same modulator topology. The main modulator has been optimized for low‐noise, neural‐action‐potential detection in the 300 Hz–6 kHz band, with an input‐referred noise of 5.0 μVrms, and occupies an area of 0.0045 mm2. An alternative configuration features a larger input stage to reduce low‐frequency noise, achieving 8.7 μVrms in the 1 Hz–10 kHz band, and occupies an area of 0.006 mm2. The modulator is powered at 1.8 V with an estimated power consumption of 3.5 μW.
Developmental network topology and electrophysiological disruptions induced by NOS1AP overexpression in in-vitro neuronal networks
Marishi, Aayush; Xue, Xiaohan; Gänswein, Tobias; et al. (2024)
Abstracts of Papers presented at the 2024 Meeting on Development & 3-D Modeling of the Human Brain
Mesoscale circuit shaping with high-density microelectrode arrays
Bossard , Yannaël; Sava, Rachel; Bartram, Julian; et al. (2025)
MEA 2025 : 13th Meeting on Neural and Electrogenic Cell Interfacing
A Time-Domain Readout Technique for Neural Interfaces Based on VCO-Timestamping
Cardes Garcia, Fernando; Azizi, Ebrahim; Hierlemann, Andreas (2023)
IEEE Transactions on Biomedical Circuits and Systems
CMOS neural interfaces are aimed at studying the electrical activity of neurons and may help to restore lost functions of the nervous system in the future. The central function of most neural interfaces is the detection of extracellular electrical potentials by means of numerous microelectrodes positioned in close vicinity to the neurons. Modern neural interfaces require compact low-power, low-noise readout circuits, capable of recording from thousands of electrodes simultaneously without excessive area consumption and heat dissipation. In this article, we propose a novel readout technique for neural interfaces. The readout is based on a voltage-controlled oscillator (VCO), the frequency of which is modulated by the input voltage. The novelty of this work lies in the postprocessing of the VCO output, which is based on generating digital timestamps that contain temporal information about the oscillation. This method is potentially advantageous, because it requires mostly digital circuitry, which is more scalable than analog circuitry. Furthermore, most of the digital circuitry required for VCO-timestamping can be shared among several VCOs, rendering the architecture efficient for multi-channel architectures. This article introduces the VCO-timestamping concept, including theoretical derivations and simulations, and presents measurements of a prototype fabricated in 0.18-μm CMOS technology. The measured input-referred noise in the 300 Hz–5 kHz band was 5.7 μ V rms , and the prototype was able to detect pre-recorded extracellular action potentials.
A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors
Cartiglia, Matteo; Costa, Filippo; Narayanan, Shyam; et al. (2023)
Research Square
Bio-signal sensing represents a pivotal domain in the medical applications of bioelectronics. Traditional methods have, so far, focused on capturing these signals as accurately as possible, leading to high sampling rates in clocked synchronous architectures. Given the sparse activity of bio-signals, this approach often results in large amounts of digitized data with no relevant information and in a significant amount of energy consumed during transmission. Here, we introduce a self-clocked microelectrode array (MEA) that senses and digitizes bio-signals at the pixel level by encoding their changes as asynchronous digital address-events, significantly reducing the amount of data that needs to be transmitted off-chip. This novel MEA comprises an array of 64 × 64 electrodes, an asynchronous 2D-arbiter, and an Address-Event Representation (AER) communication block. Each pixel within the array operates autonomously, monitoring input signals for relative voltage fluctuations instigated by cellular activity. Upon detecting a sufficiently large signal change, the pixel produces a digital pulse encoded with its corresponding address. Positive signal changes are encoded as “up” events, while negative ones are encoded as “down” events and, upon generation, are routed off-chip instantly via the asynchronous arbiter. Here, we present results from the chip characterization and experimental measurements using electrogenic cells. Moreover, we interface the MEA to a mixed-signal neuromorphic processor, demonstrating a prototype for end-to-end event-based bio-signal sensing and processing.
Circuit-Based Design of Microfluidic Drop Networks
Rousset, Nassim; Lohasz, Christian; Boos, Julia Alicia; et al. (2022)
Micromachines
Microfluidic-drop networks consist of several stable drops-interconnected through microfluidic channels-in which organ models can be cultured long-term. Drop networks feature a versatile configuration and an air-liquid interface (ALI). This ALI provides ample oxygenation, rapid liquid turnover, passive degassing, and liquid-phase stability through capillary pressure. Mathematical modeling, e.g., by using computational fluid dynamics (CFD), is a powerful tool to design drop-based microfluidic devices and to optimize their operation. Although CFD is the most rigorous technique to model flow, it falls short in terms of computational efficiency. Alternatively, the hydraulic-electric analogy is an efficient "first-pass" method to explore the design and operation parameter space of microfluidic-drop networks. However, there are no direct electric analogs to a drop, due to the nonlinear nature of the capillary pressure of the ALI. Here, we present a circuit-based model of hanging- and standing-drop compartments. We show a phase diagram describing the nonlinearity of the capillary pressure of a hanging drop. This diagram explains how to experimentally ensure drop stability. We present a methodology to find flow rates and pressures within drop networks. Finally, we review several applications, where the method, outlined in this paper, was instrumental in optimizing design and operation.

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Fernando Cardes Garcia

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