Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions.

Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.

Enhanced selectivity and recovery of phosphate and nitrate ions onto coffee ground waste biochars via co-precipitation of Mg/Al layered double hydroxides: A potential slow-release fertilizer.

In this study, the feasibility of Mg/Al layered double hydroxides (LDH) functionalized coffee ground waste biochars (LDHMgAl@CWGB) as a potential adsorbent to selectively recover phosphate (PO43-) and nitrate (NO3-) ions in aqueous phases and their consecutive uses as a slow-release fertilizer for stimulating the plant growth were identified. The higher adsorption capacity of PO43- and NO3- ions by LDHMgAl@CWGB (PO43- = 6.98 mgP/g, NO3- = 2.82 mgN/g) compared with pristine coffee ground waste biochars (CWGB; PO43- = 0.19 mgP/g, NO3- = 0.32 mgN/g) was mainly due to the incorporation of Mg/Al mixed oxides and Cl contents. Chemisorption and intra-particle mainly controlled the adsorptive recovery of PO43- and NO3- ions by CWGB and LDHMgAl@CWGB in aqueous phases and their adsorption toward CWGB and LDHMgAl@CWGB proceeded endothermically and spontaneously. The changes in the major adsorption mechanisms of PO43- and NO3- ions from ligand exchange (CWGB) to electrostatic surface complexation and anion-exchange (LDHMgAl@CWGB) supported the conclusion that the alternation of the surface features through Mg/Al LDH functionalization might improve selectivity and adsorption capacity of PO43- and NO3- ions onto CWGB under the co-existence of Cl-, SO42-, and HCO3- ions. Since PO43-- and NO3--loaded LDHMgAl@CWGB exhibited much higher seed germination, root and shoot growth rates of garden cress seeds (Lepidium sativum L) than other liquid and solid matrices, including 5 mgP/L PO43- and 5 mgN/L NO3-, 10 mgP/L PO43- and 10 mgN/L NO3-, and LDHMgAl@CWGB, it can be postulated that PO43-- and NO3--loaded LDHMgAl@CWGB could be practically applicable to the agricultural field as a slow-release fertilizer to facilitate the seed germination, root and shoot growth of the plants.

Effects of L-Type Voltage-Gated Calcium Channel (LTCC) Inhibition on Hippocampal Neuronal Death after Pilocarpine-Induced Seizure.

Epilepsy, marked by abnormal and excessive brain neuronal activity, is linked to the activation of L-type voltage-gated calcium channels (LTCCs) in neuronal membranes. LTCCs facilitate the entry of calcium (Ca2+) and other metal ions, such as zinc (Zn2+) and magnesium (Mg2+), into the cytosol. This Ca2+ influx at the presynaptic terminal triggers the release of Zn2+ and glutamate to the postsynaptic terminal. Zn2+ is then transported to the postsynaptic neuron via LTCCs. The resulting Zn2+ accumulation in neurons significantly increases the expression of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase subunits, contributing to reactive oxygen species (ROS) generation and neuronal death. Amlodipine (AML), typically used for hypertension and coronary artery disease, works by inhibiting LTCCs. We explored whether AML could mitigate Zn2+ translocation and accumulation in neurons, potentially offering protection against seizure-induced hippocampal neuronal death. We tested this by establishing a rat epilepsy model with pilocarpine and administering AML (10 mg/kg, orally, daily for 7 days) post-epilepsy onset. We assessed cognitive function through behavioral tests and conducted histological analyses for Zn2+ accumulation, oxidative stress, and neuronal death. Our findings show that AML's LTCC inhibition decreased excessive Zn2+ accumulation, reactive oxygen species (ROS) production, and hippocampal neuronal death following seizures. These results suggest amlodipine's potential as a therapeutic agent in seizure management and mitigating seizures' detrimental effects.

Effects of Pyruvate Kinase M2 (PKM2) Gene Deletion on Astrocyte-Specific Glycolysis and Global Cerebral Ischemia-Induced Neuronal Death.

Ischemic stroke is caused by insufficient blood flow to the brain. Astrocytes have a role in bidirectionally converting pyruvate, generated via glycolysis, into lactate and then supplying it to neurons through astrocyte-neuron lactate shuttle (ANLS). Pyruvate kinase M2 (PKM2) is an enzyme that dephosphorylates phosphoenolpyruvate to pyruvate during glycolysis in astrocytes. We hypothesized that a reduction in lactate supply in astrocyte PKM2 gene deletion exacerbates neuronal death. Mice harboring a PKM2 gene deletion were established by administering tamoxifen to Aldh1l1-CreERT2; PKM2f/f mice. Upon development of global cerebral ischemia, mice were immediately injected with sodium l-lactate (250 mg/kg, i.p.). To verify our hypothesis, we compared oxidative damage, microtubule disruption, ANLS disruption, and neuronal death between the gene deletion and control subjects. We observed that PKM2 gene deletion increases the degree of neuronal damage and impairment of lactate metabolism in the hippocampal region after GCI. The lactate administration groups showed significantly reduced neuronal death and increases in neuron survival and cognitive function. We found that lactate supply via the ANLS in astrocytes plays a crucial role in maintaining energy metabolism in neurons. Lactate administration may have potential as a therapeutic tool to prevent neuronal damage following ischemic stroke.

MONETA: A Processing-In-Memory-Based Hardware Platform for the Hybrid Convolutional Spiking Neural Network With Online Learning.

We present a processing-in-memory (PIM)-based hardware platform, referred to as MONETA, for on-chip acceleration of inference and learning in hybrid convolutional spiking neural network. MONETAuses 8T static random-access memory (SRAM)-based PIM cores for vector matrix multiplication (VMM) augmented with spike-time-dependent-plasticity (STDP) based weight update. The spiking neural network (SNN)-focused data flow is presented to minimize data movement in MONETAwhile ensuring learning accuracy. MONETAsupports on-line and on-chip training on PIM architecture. The STDP-trained convolutional neural network within SNN (ConvSNN) with the proposed data flow, 4-bit input precision, and 8-bit weight precision shows only 1.63% lower accuracy in CIFAR-10 compared to the STDP accuracy implemented by the software. Further, the proposed architecture is used to accelerate a hybrid SNN architecture that couples off-chip supervised (back propagation through time) and on-chip unsupervised (STDP) training. We also evaluate the hybrid network architecture with the proposed data flow. The accuracy of this hybrid network is 10.84% higher than STDP trained accuracy result and 1.4% higher compared to the backpropagated training-based ConvSNN result with the CIFAR-10 dataset. Physical design of MONETAin 65 nm complementary metal-oxide-semiconductor (CMOS) shows 18.69 tera operation per second (TOPS)/W, 7.25 TOPS/W and 10.41 TOPS/W power efficiencies for the inference mode, learning mode, and hybrid learning mode, respectively.

Artificial van der Waals hybrid synapse and its application to acoustic pattern recognition.

Brain-inspired parallel computing, which is typically performed using a hardware neural-network platform consisting of numerous artificial synapses, is a promising technology for effectively handling large amounts of informational data. However, the reported nonlinear and asymmetric conductance-update characteristics of artificial synapses prevent a hardware neural-network from delivering the same high-level training and inference accuracies as those delivered by a software neural-network. Here, we developed an artificial van-der-Waals hybrid synapse that features linear and symmetric conductance-update characteristics. Tungsten diselenide and molybdenum disulfide channels were used selectively to potentiate and depress conductance. Subsequently, via training and inference simulation, we demonstrated the feasibility of our hybrid synapse toward a hardware neural-network and also delivered high recognition rates that were comparable to those delivered using a software neural-network. This simulation involving the use of acoustic patterns was performed with a neural network that was theoretically formed with the characteristics of the hybrid synapses.

Graphene-assisted spontaneous relaxation towards dislocation-free heteroepitaxy.

Although conventional homoepitaxy forms high-quality epitaxial layers1-5, the limited set of material systems for commercially available wafers restricts the range of materials that can be grown homoepitaxially. At the same time, conventional heteroepitaxy of lattice-mismatched systems produces dislocations above a critical strain energy to release the accumulated strain energy as the film thickness increases. The formation of dislocations, which severely degrade electronic/photonic device performances6-8, is fundamentally unavoidable in highly lattice-mismatched epitaxy9-11. Here, we introduce a unique mechanism of relaxing misfit strain in heteroepitaxial films that can enable effective lattice engineering. We have observed that heteroepitaxy on graphene-coated substrates allows for spontaneous relaxation of misfit strain owing to the slippery graphene surface while achieving single-crystalline films by reading the atomic potential from the substrate. This spontaneous relaxation technique could transform the monolithic integration of largely lattice-mismatched systems by covering a wide range of the misfit spectrum to enhance and broaden the functionality of semiconductor devices for advanced electronics and photonics.

A Neuromorphic Device Implemented on a Salmon-DNA Electrolyte and its Application to Artificial Neural Networks.

A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+-doped salmon deoxyribonucleic acid (S-DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S-DNA electrolyte, the synaptic operation of the S-DNA device features special long-term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S-DNA-based neural networks. Furthermore, the representative neuronal operation, "integrate-and-fire," is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S-DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (|αp|: 31→20, |αd|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single-layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S-DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy.