Search Results (97)

This paper presents a highly linear second-order, tunable bandpass filter (BPF) based on low-Gm operational transconductance amplifiers (OTAs) to be used in a single-frequency bioimpedance analysis (SFBIA) system. The center frequency of the proposed BPF can be set at the target center frequency $f_c$ = 1 kHz through a tuning current $I_C$, thus compensating for process variations and temperature changes after fabrication. A low quality factor is established to let the input signal pass through without significant phase shift or gain fluctuation, while reducing the out-of-band noise contribution to the readout circuit. The proposed BPF was implemented in a 0.18 $\mathsf{\mu }$m CMOS process and consumes 2.6 $\mathsf{\mu }$W from a 1.8 V supply. The Gm reduction technique enables precise control of the OTA’s transconductance, and therefore of the center frequency of the BPF. For $I_C$ ranging from 28 nA to 138 nA, the measured $f_c$ varies from 690 Hz to 3 kHz, with a constant Q factor of approximately 0.4. The proposed BPF can handle input signals as high as 132 mV_pp with a maximum 1% total harmonic distortion. Full article

Major depressive disorder (MDD) is not reducible to a single neurotransmitter deficit. Current explanations commonly involve monoaminergic dysfunction, hypothalamic–pituitary–adrenal axis dysregulation, immune-inflammatory activation, impaired neuroplasticity and synaptic dysfunction, together with metabolic and neurovascular abnormalities. Lipidomic studies have repeatedly identified glycerophospholipid abnormalities in MDD, but their mechanistic meaning remains unresolved because changes in bulk lipid abundance do not explain how altered lipid metabolism becomes a receptor-level neural signal. This review develops a testable interpretation of the lysophosphatidylcholine (LPC)–autotaxin (ATX)–lysophosphatidic acid (LPA)–LPA receptor (LPAR) axis in which LPC species generated during phospholipid turnover provide ATX substrates, ATX activity determines local LPA generation, LPA production and inactivation shape ligand availability, and LPAR signaling links the lipid product to neural output. This structure shifts the focus from total lipid abundance to matched assessment of lipid species, enzyme activity, anatomical site and receptor subtype. Human studies report lower serum and cerebrospinal fluid (CSF) ATX in MDD, lower CSF LPA 22:6 in MDD and schizophrenia, and negative total LPA findings that caution against biomarker oversimplification. Depression-relevant and broader stress- or anxiety-related experimental studies show that ATX, LPA and LPAR perturbation can affect hippocampal function, synaptic physiology, emotional behavior and stress resilience. The key unresolved issue is whether brain-accessible LPC species, active ATX, locally generated LPA, LPA inactivation capacity and receptor-specific output can be demonstrated within the same MDD-relevant fluid, brain-interface site or neural circuit. Future work should therefore move from fluid-level association toward pathway closure through targeted and spatial lipidomics, anatomical ATX activity mapping, LPA inactivation assays, blood–brain barrier (BBB)/interface analysis, LPAR perturbation and matched circuit or behavioral readouts. Full article

Analog in-memory computing (AIMC) has emerged as a promising approach to mitigate the Von Neumann bottleneck in matrix operations, which are common in deep learning applications. However, the practical implementation of resistive crossbar arrays is limited by challenges in signed weight representation, conductance quantization, and device nonlinearity. This paper presents a differential mixed-signal architecture for accurate signed matrix–vector multiplication (MVM), integrated with a RISC-V microcontroller for edge inference applications. A structured digital-to-analog mapping framework encodes quantized neural network weights into programmable conductance values while preserving arithmetic correctness. The design employs voltage-mode input encoding, differential current summation, and transimpedance-based readout followed by analog-to-digital conversion, enabling single-cycle signed accumulation without duplicating crossbar resources. A 32 × 16 dual-layer prototype crossbar was fabricated and experimentally characterized. Measurements demonstrate a mean absolute percentage error (MAPE) below 1% within the linear operating region and below 4% over the full-scale conductance range. These results validate the robustness of the proposed mapping methodology and confirm the feasibility of hybrid analog–digital acceleration for edge AI systems. Consequently, this discrete prototype serves as a physical verification platform for the AIMC approach, providing valuable insights for more efficient mixed-signal computing integrated circuit (IC) designs. Full article

Temporal lobe epilepsy (TLE) is a common drug-resistant epilepsy characterized by recurrent seizures, cognitive impairment, aberrant adult hippocampal neurogenesis, inhibitory circuit disruption, and persistent inflammatory remodeling. Current anti-seizure medications primarily offer symptomatic control and do not target the progressive structural and functional deterioration of epileptic hippocampal networks. Here, we investigated whether local nerve growth factor (NGF)-hydrogel delivery during the latent phase after status epilepticus could mitigate hippocampal pathological remodeling and improve long-term outcomes in a kainic acid (KA)-induced mouse model (utilizing C57BL/6J and Nestin-CreERT2 mice). Animals were randomly assigned to three groups: the saline control group, the untreated KA epilepsy group, and the KA + NGF-hydrogel treatment group. NGF-hydrogel was administered into hippocampal Cornu Ammonis 1 (CA1) beginning 3 days post-kainic acid and repeated every 15 days. Histological, immunofluorescence, circuit-tracing, electrophysiology, electroencephalography (EEG), and behavioral assessments were used to evaluate neurogenesis, microenvironment, circuit readouts, seizure burden, and cognition. NGF-hydrogel treatment was associated with preserved dentate gyrus neural stem cell populations, improved newborn granule cell localization and maturation, attenuated neuroinflammation and gliosis, and partial recovery of inhibitory interneuron markers. These changes were accompanied by improved hippocampal circuit readouts, reduced chronic spontaneous seizure burden, and enhanced recognition and spatial memory. Our findings indicate that local NGF-hydrogel delivery following status epilepticus is associated with improved hippocampal remodeling and functional outcomes, and suggest that biomaterial-based neurotrophic support may be a promising strategy for providing targeted neuroprotection and facilitating excitatory/inhibitory (E/I) balance reconstruction in the epileptic hippocampus. Full article

The selection of the right topology (ansatz) for a Variational Quantum Algorithm (VQA) is a complex task that usually involves deep knowledge of a particular problem. The importance of the selection is greater when we consider the current state of quantum hardware, particularly the noise associated with the complexity of Variational Quantum Circuits (VQCs) that implement VQAs. Here, a comparison is presented between two confronted approaches for solving the MaxCut problem: QAOA, which has a theoretical proof of convergence, and the automatic design proposal (QNAS), which relies on evolutionary algorithms (NSGA-II) to discover efficient circuits. The comparison was made across 490 graph instances from different graph topologies and sizes ($n=4$ to $n=16$), accounting for noise models such as depolarizing noise, gate errors, and readout noise. The results show that QAOA achieves an approximation ratio ($r_A$) $\approx 1$ on complete graphs at the cost of being almost 12 times more complex than QNAS in ideal conditions while approaching the random noise floor ($r_A\approx 0.5)$. QNAS was capable of finding circuits less complex while maintaining $69\%$ of the fidelity at a cost of having an $r_A$ on the interval $0.7\le r_A\le 0.8$. However, when the comparison is made across sparse graphs, performance is comparable, while QNAS is less complex. Full article

The inherent bottleneck of the von Neumann architecture and the limited power budget of edge devices necessitate energy-efficient hardware solutions for artificial intelligence. Memristor-based In-Memory Computing (IMC) has emerged as a promising candidate; however, the high-power consumption of peripheral circuits, particularly Analog-to-Digital Converters (ADCs), and the reliability issues of memristive devices remain significant challenges. In this paper, we propose a hybrid Convolutional Spiking Neural Network (CSNN) architecture designed for resource-constrained edge computing. Our approach integrates digital Non-Leaky Integrate-and-Fire (NLIF) neurons with Knowm Self-Directed Channel (SDC) memristor-based synapses in a 1T1R crossbar array. To maximize power efficiency, we replace conventional high-resolution ADCs with a streamlined readout circuit utilizing a Current Sense Amplifier (CSA) and a 1-bit comparator. Furthermore, we employ an intensity-to-latency temporal coding scheme to minimize spike activity and mitigate device endurance degradation. We validated the proposed system using the MNIST dataset, achieving a classification accuracy of 97.8%, which is comparable to state-of-the-art floating-point SNNs using supervised learning methods. Power analysis confirms that our 1-bit readout method consumes only 18.4% of the energy required by an 8-bit ADC-based approach while maintaining negligible accuracy loss. Additionally, the deterministic single-spike nature of our temporal coding significantly reduces write stress on memristors compared to rate coding. These results demonstrate that the proposed hybrid CSNN offers a robust and energy-efficient solution for neuromorphic edge intelligence. Full article

This work presents an advanced electro-physical model for hydrogenated amorphous silicon (a-Si:H) Junction Field Effect Transistors (JFETs) to enable the design of devices with energy-efficient analog interface building blocks for Lab-on-Chip (LoC) systems. The presence of this device can support monolithic integration with thin-film sensors and circuit-level design through a validated compact formulation. The model accurately describes the behavior of a-Si:H JFETs addressing key physical phenomena, such as the channel thickness dependence on the gate-source voltage when the channel approaches full depletion. A comprehensive framework was developed, integrating experimental data and mathematical refinements to ensure robust predictions of JFET performance across operating regimes, including the transition toward full depletion and the associated current-limiting behavior. The model was validated through a broad set of fabricated devices, demonstrating excellent agreement with experimental data in both the linear and saturation regions. Specifically, the validation was carried out at 25 °C on 15 fabricated JFET configurations (12 nominally identical devices per configuration), using the mean characteristics of 9 devices with standard-deviation error bars. In the investigated bias range, the devices operate in a sub-µA regime (up to several hundred nA), which naturally supports µW-level dissipation for low-power interfaces. This work provides a compact, experimentally validated modeling basis for the design and optimization of a-Si:H JFET-based LoC front-end/readout circuits within technology-constrained and energy-efficient operating conditions. Full article

Major depressive disorder (MDD) was long framed as a single clinical entity arising from a linear stress–monoamine–hypothalamic–pituitary–adrenal (HPA) axis cascade. This view was shaped by forced swim and learned helplessness tests in animals and by short-term symptom-based trials using scales such as the Hamilton Depression Rating Scale (HAM-D) and the Montgomery–Åsberg Depression Rating Scale (MADRS). This “unitary cascade” view has been dismantled by advances in neuroimaging, immune–metabolic profiling, sleep phenotyping, and plasticity markers, which reveal divergent circuit-level, inflammatory, and chronobiological patterns across anxiety-linked, pain-burdened, and cognitively weighted depressive presentations, all characterized by high rates of non-response and relapse. Translationally, face-valid rodent assays that equated immobility with despair have yielded limited bedside benefit, whereas cross-species bridges—electroencephalography (EEG) motifs, rapid eye movement (REM) architecture, effort-based reward tasks, and inflammatory/metabolic panels—are beginning to provide mechanistically grounded, clinically actionable readouts. In current practice, depression care is shifting toward systems psychiatry: inflammation-high and metabolic-high archetypes, anhedonia- and circadian-dominant subgroups, formal treatment-resistant depression (TRD) staging, connectivity-guided neuromodulation, esketamine, selected pharmacogenomic panels, and early digital phenotyping, as endpoints broaden to functioning and durability. A central gap is that heterogeneity is acknowledged but rarely built into trial design or implementation. This perspective advances a plasticity-centered systems psychiatry in which a testable prediction is that manipulating defined prefrontal–striatal and prefrontal–limbic circuits in sex-balanced, chronic-stress models will reproduce human network-defined biotypes and treatment response, and proposes hybrid effectiveness–implementation platforms that embed immune–metabolic and sleep panels, circuit-sensitive tasks, and digital monitoring under a shared, preregistered data standard. Full article

For the application of a high-performance shortwave infrared (SWIR) detector, a fully integrated analog front-end (AFE) circuit is proposed in this paper, which includes a readout integrated circuit (ROIC) and a 12-bit/100 kHz two-step single-slope analog-to-digital converter (TS-SS ADC). The ROIC adopts a direct injection (DI) structure with a pixel size of only 10 µm × 10 µm. The column processing circuit uses a passive correlated double-sampling (CDS) circuit to reduce noise and improve dynamic range. The comparator of four inputs in the ADC solves the problem of linearity reduction caused by charge redistribution during coarse quantization. In addition, the current steering digital-to-analog converter (DAC) is used to compensate for the non-ideal characteristics of the switch, which effectively optimizes the differential nonlinearity (DNL) and integral nonlinearity (INL). The AFE is implemented using SMIC 180 nm 1P6M technology. The post-simulation results show that at a power supply voltage of 3.3 V, the AFE has a frame rate of 100 Hz and a full well capacity (FWC) of 2.8 Me⁻. The linearity can reach 99.59%, and the equivalent output noise is 243 µV. The dynamic range is 73.8 dB. Meanwhile, the signal-to-noise distortion ratio (SNDR) and effective number of bits (ENOB) are 68.38 dB and 11.06 bits, respectively. Full article

This article proposes a compact model for the radiation-sensitive field-effect transistor (RADFET). The model represents the basic I–V characteristics of the MOSFET device and includes the effects of threshold voltage shift as a function of absorbed dose and gate bias, gate tunneling current, gate radiation current, dose accumulation, and fading. It accurately represents high-field effects in the gate dielectric of the RADFET device. Both the Fowler–Nordheim gate tunneling current and the radiation-induced gate current are incorporated into the model. The model enables determination of the radiation-induced charge during RADFET operation under high-field injection conditions, thereby improving the precision of accumulated dose readout. The proposed model is fully compatible with SPICE-based circuit simulators and can represent any type of RADFET device, including those with high-k gate dielectrics. Furthermore, the model was developed entirely using open-source circuit simulation tools. Full article

This work presents a low-power photoplethysmography (PPG) readout integrated circuit (IC) that achieves a wide dynamic range (DR) through the direct integration of a voltage-controlled oscillator (VCO)-based quantizer into the photodiode driver. Conventional PPG readout circuits rely on either transimpedance amplifier (TIA) or light-to-digital converter (LDC) topologies, both of which require auxiliary DC suppression loops. These additional loops not only raise power consumption but also limit the achievable DR. The proposed design eliminates the need for such circuits by embedding a linear regulator with a mirroring scale calibrator and a time-domain quantizer. The quantizer provides first-order noise shaping, enabling accurate extraction of the AC PPG signal while the regulator directly handles the large DC current component. Post-layout simulations show that the proposed readout achieves a signal-to-noise-and-distortion ratio (SNDR) of 40.0 dB at 10 µA DC current while consuming only 0.80 µW from a 2.5 V supply. The circuit demonstrates excellent stability across process–voltage–temperature (PVT) corners and maintains high accuracy over a wide DC current range. These features, combined with a compact silicon area of 0.725 mm² using TSMC 250 nm bipolar–CMOS–DMOS (BCD) process, make the proposed IC an attractive candidate for next-generation wearable and biomedical sensing platforms. Full article

Thermal mass flow sensors (TMFS) are used to detect the flow rates of gases. TMFS elements are available in different technologies and, depending on the one used, the material choice of substrate, heater, and temperature sensors can limit their performance. In this work, a sensor element based on the Ensinger Microsystems Technology (EMST) is presented that uses PEEK as the substrate, nickel-chromium as the heater, and nickel as the temperature sensor material. The fabrication process of the element is described, the completion to a flow sensor with a control and readout circuit based on discharge time measurement with picosecond resolution is presented, and measurement results are shown, which are compared to sensors with a commercially available element based on thin film technology on ceramic and an element built with discrete components, all using the same electronics. It is shown that the operation of all sensor elements with the proposed readout circuit was successful, flow-dependent signals were achieved, and the performance of TMFS in EMST improved. Its heater shows better results compared to the commercial element due to material choice with a smaller temperature coefficient of resistance. In its current state, the TMFS in EMST is suitable to detect flow rates > 20 SLPM. The performance needs to be improved further, since the temperature sensors still differ too much from another. Full article

This paper proposes a dual-mode CMOS analog front-end (AFE) circuit for short-wave infrared (SWIR) image sensors, which integrates a hybrid readout circuit (ROIC) and a 12-bit two-step single-slope analog-to-digital converter (TS-SS ADC). The ROIC dynamically switches between buffered-direct-injection (BDI) and direct-injection (DI) modes, thus balancing injection efficiency against power consumption. While the DI structure offers simplicity and low power, it suffers from unstable biasing and reduced injection efficiency under high background currents. Conversely, the BDI structure enhances injection efficiency and bias stability via an input buffer but incurs higher power consumption. To address this trade-off, a dual-mode injection architecture with mode-switching transistors is implemented. Mode selection is executed in-pixel via a low-leakage transmission gate and coordinated by the column timing controller, enabling low-current pixels to operate in low-noise BDI mode, whereas high-current pixels revert to the low-power DI mode. The TS-SS ADC employs a four-terminal comparator and dynamic reference voltage compensation to mitigate charge leakage and offset, which improves signal-to-noise ratio (SNR) and linearity. The prototype occupies 2.1 mm × 2.88 mm in a 0.18 µm CMOS process and serves a 64 × 64 array. The AFE achieves a dynamic range of 75.58 dB, noise of 249.42 μV, and 81.04 mW power consumption. Full article

Infrared image sensors are crucial across various industries. However, with technological advancements, the growing scale of infrared image sensors has made the impact of transient dose rate effects increasingly significant. It is necessary to conduct relevant radiation effect studies to provide the theoretical and data basis for future radiation-hardened design. This study explores the response of large-area N-wells in the readout circuit of infrared detectors to transient dose rate effects. The TCAD simulation results indicate that the expansive N-well area in the merged-design pixel units generates significant current pulses when exposed to gamma-ray irradiation. Specifically, at dose rates of 3 × 10¹¹ rad/s, 5 × 10¹¹ rad/s, 7 × 10¹¹ rad/s, and 9 × 10¹¹ rad/s, the pulse currents measured are 39 nA, 64 nA, 89 nA, and 119 nA, respectively. Due to the spatial constraints of the 55 nm merged design, the close proximity of the GND to the N-well creates a high potential barrier near the N-well, obstructing the path between the GND and the substrate, which results in the pulse current exhibiting a stepped-like characteristic. Full article

This paper reviews various design approaches for sensing schemes that utilize silicon nanowire (SiNW) ion-sensitive field-effect transistors (ISFETs) for pH-sensing applications. SiNW ISFETs offer advantageous characteristics, including a high surface-to-volume ratio, fast response time, and suitability for integration with complementary metal oxide semiconductor (CMOS) technology. This review focuses on SiNW ISFET-based biosensors in three key aspects: (1) major fabrication processes and device structures; (2) theoretical analysis of key performance parameters in readout circuits such as sensitivity, linearity, noise immunity, and output range in different system configurations; and (3) an overview of existing readout circuits with quantitative evaluations of N-type and P-type current-mirror-based circuits, highlighting their strengths and limitations. Finally, this paper proposes a modified N-type readout scheme integrating an operational amplifier with a negative feedback network to overcome the low sensitivity of conventional N-type circuits. This design enhances gain control, linearity, and noise immunity while maintaining stability. These advancements are expected to contribute to the advancement of the current state-of-the-art SiNW ISFET-based readout circuits. Full article