Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing.

Recently, the increasing demand for data-centric applications is driving the elimination of image sensing, memory and computing unit interface, thus promising for latency- and energy-strict applications. Although dedicated electronic hardware has inspired the development of in-memory computing and in-sensor computing, folding the entire signal chain into one device remains challenging. Here an in-memory sensing and computing architecture is demonstrated using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is realized based on the multiplications of light power and photoresponsivity through the photocurrent generation process and Kirchhoff's law. The weight is stored and programmed locally by the ferroelectric domains, enabling 51 (>5 bit) distinguishable weight states with linear, symmetric and reversible manipulation characteristics. Image recognition can be performed without any external memory and computing units. The three-in-one paradigm, integrating high-level computing, weight memorization and high-performance sensing, paves the way for a computing architecture with low energy consumption, low latency and reduced hardware overhead.

Low complexity joint clock recovery and adaptive equalization based on a baud-rate timing phase error detector for short-reach digital coherent transmission.

Digital coherent receivers adopting joint clock recovery and adaptive equalization (JCA) can avoid failures of the adaptive equalizer (AEQ) or clock recovery algorithm (CRA) due to clock asynchrony and chromatic dispersion (CD). But in the previous JCA scheme, the AEQ has a heavy computational load as it has to generate two samples per symbol (SPS) for the subsequent timing phase error detector (TPED) which is the core of the CRA. Furthermore, the previous JCA scheme cannot compensate for receiver skew or accommodate Nyquist signals with small roll-off factors (ROFs). These shortcomings hinder its practical applications in ultrahigh-speed short-reach coherent transmission requiring low power consumption, high spectral efficiency, whilst being sensitive to receiver skew. To solve this problem, we propose a new JCA scheme by integrating a two-section real-valued (RV) AEQ with an all-digital feedback CRA based on a baud-rate TPED versatile for different ROFs. Experiments for 61-GBaud dual-polarization (DP) Nyquist 16QAM signals with an ROF of 0.01 show that, compared with the previous JCA scheme, the proposed scheme can reduce the AEQ computational load by about 70% for 10-km transmission, whilst improving the receiver sensitivity by more than 1.7 dB for a receiver skew of 1.5 ps. As far as we know, the proposed JCA scheme is the most comprehensive and efficient solution for ultrahigh-speed short-reach coherent transmission where CD, receiver skew, clock asynchrony, and Nyquist signals with small ROFs have to be dealt with.

Time-domain low-complexity clock recovery for non-integer oversampled Nyquist signals with a small roll-off factor.

A clock recovery algorithm (CRA) suitable for non-integer oversampled Nyquist signals with a small roll-off factor (ROF) is appealing to short-reach high-speed inter-datacenter transmission systems which need to cut down the transceiver power consumption and cost by reducing the oversampling factor (OSF) and using cheap low-bandwidth components. However, due to the lack of a suitable timing phase error detector (TPED), CRAs proposed now fail for non-integer OSFs below two and small ROFs close to zero and are not hardware-efficient. To solve these problems, we propose a low-complexity TPED by modifying the time-domain quadratic signal and reselecting the synchronization spectral component. We demonstrate that the proposed TPED, in combination with a piece-wise parabolic (PWP) interpolator, can significantly improve the performance of feedback CRAs for non-integer oversampled Nyquist signals with a small ROF. Numerical simulations and experiments show that, based on the improved CRA, the receiver sensitivity penalty can keep below 0.5 dB when the OSF is reduced from 2 to 1.25 and the ROF is varied from 0.1 to 0.001 for 45 GBaud dual-polarization Nyquist 16QAM signals.

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-Assisted Stimulated Raman Molecular Cytology.

Peritoneal metastasis (PM) is the mostcommon form of distant metastasis and one of the leading causes of death in gastriccancer (GC). For locally advanced GC, clinical guidelines recommend peritoneal lavage cytology for intraoperative PM detection. Unfortunately, current peritoneal lavage cytology is limited by low sensitivity (<60%). Here the authors established the stimulated Raman molecular cytology (SRMC), a chemical microscopy-based intelligent cytology. The authors firstly imaged 53 951 exfoliated cells in ascites obtained from 80 GC patients (27 PM positive, 53 PM negative). Then, the authors revealed 12 single cell features of morphology and composition that are significantly different between PM positive and negative specimens, including cellular area, lipid protein ratio, etc. Importantly, the authors developed a single cell phenotyping algorithm to further transform the above raw features to feature matrix. Such matrix is crucial to identify the significant marker cell cluster, the divergence of which is finally used to differentiate the PM positive and negative. Compared with histopathology, the gold standard of PM detection, their SRMC method could reach 81.5% sensitivity, 84.9% specificity, and the AUC of 0.85, within 20 minutes for each patient. Together, their SRMC method shows great potential for accurate and rapid detection of PM from GC.

Short-term synaptic plasticity in emerging devices for neuromorphic computing.

Neuromorphic computing is a promising computing paradigm toward building next-generation artificial intelligence machines, in which diverse types of synaptic plasticity play an active role in information processing. Compared to long-term plasticity (LTP) forming the foundation of learning and memory, short-term plasticity (STP) is essential for critical computational functions. So far, the practical applications of LTP have been widely investigated, whereas the implementation of STP in hardware is still elusive. Here, we review the development of STP by bridging the physics in emerging devices and biological behaviors. We explore the computational functions of various STP in biology and review their recent progress. Finally, we discuss the main challenges of introducing STP into synaptic devices and offer the potential approaches to utilize STP to enrich systems' capabilities. This review is expected to provide prospective ideas for implementing STP in emerging devices and may promote the construction of high-level neuromorphic machines.

High receiver skew-tolerant and hardware-efficient clock recovery for short-reach coherent transmission.

The performance of the high-baud-rate and high-order-modulation-format short-reach coherent transmission systems is sensitive to the in-phase and quadrature (IQ) skew. The conventional receiver IQ skew compensation schemes based on adaptive equalizers (AEQs) suffer from the IQ skew enhanced timing jitter incurred by the clock recovery algorithm (CRA), resulting in a serious sensitivity degradation. In this paper, we first propose a novel multiplication-free timing phase error detector (TPED) with the gain insensitive to the receiver IQ skew and the capability to deal with the complex-valued Nyquist signal with an arbitrary roll-off factor and its real-valued IQ tributaries. Based on the TPED, we then propose a new all-digital feedback CRA able to compensate for the receiver IQ skew. With the novel CRA, the IQ skew enhanced timing jitter is eliminated and the receiver sensitivity can be improved by more than 1 dB for the 61 GBaud dual-polarization Nyquist 16QAM system for an IQ skew of 5 ps. Furthermore, the proposed CRA can reduce the computation complexity of the AEQ by more than 25% compared with the existing schemes by relieving the AEQ from IQ skew compensation. Both numerical simulations and experiments are carried out to validate the advantages of the proposed algorithms. The high-skew-tolerant and low-complexity CRA is a strong candidate for the power-sensitive high-speed short-reach coherent transmission systems.

A bioinspired configurable cochlea based on memristors.

Cochleas are the basis for biology to process and recognize speech information, emulating which with electronic devices helps us construct high-efficient intelligent voice systems. Memristor provides novel physics for performing neuromorphic engineering beyond complementary metal-oxide-semiconductor technology. This work presents an artificial cochlea based on the shallen-key filter model configured with memristors, in which one filter emulates one channel. We first fabricate a memristor with the TiN/HfOx/TaOx/TiN structure to implement such a cochlea and demonstrate the non-volatile multilevel states through electrical operations. Then, we build the shallen-key filter circuit and experimentally demonstrate the frequency-selection function of cochlea's five channels, whose central frequency is determined by the memristor's resistance. To further demonstrate the feasibility of the cochlea for system applications, we use it to extract the speech signal features and then combine it with a convolutional neural network to recognize the Free Spoken Digit Dataset. The recognition accuracy reaches 92% with 64 channels, compatible with the traditional 64 Fourier transform transformation points of mel-frequency cepstral coefficients method with 95% recognition accuracy. This work provides a novel strategy for building cochleas, which has a great potential to conduct configurable, high-parallel, and high-efficient auditory systems for neuromorphic robots.

Multiplication-free timing phase error detector for Nyquist and non-Nyquist optical signals.

A versatile digital coherent receiver capable of handling optical signals with different kinds of pulse shaping schemes (PSSs) is indispensable for future flexible and heterogeneous coherent optical communication networks. Therefore, a low-complexity timing phase error detector (TPED) versatile for all PSSs is of particular interest. In this paper, we propose a TPED suitable for both Nyquist signals with different roll-off factors and non-Nyquist signals. It requires two samples per symbol and involves no multiplications. As far as we know, it has the lowest computation complexity compared with the existing TPEDs used in coherent systems, while incurring no receiver sensitivity penalty. Numerical simulations and experiments are carried out to demonstrate the superior timing performance and PSS versatility of the proposed TPED in both open-loop and closed-loop working conditions. We also implement the novel TPED in a field programmable gate array (FPGA) and verify its real-time clock recovery performance using the 10 Gbaud very low roll-off Nyquist and non-Nyquist quadrature phase shift keying (QPSK) signals.

Compute-in-Memory for Numerical Computations.

In recent years, compute-in-memory (CIM) has been extensively studied to improve the energy efficiency of computing by reducing data movement. At present, CIM is frequently used in data-intensive computing. Data-intensive computing applications, such as all kinds of neural networks (NNs) in machine learning (ML), are regarded as 'soft' computing tasks. The 'soft' computing tasks are computations that can tolerate low computing precision with little accuracy degradation. However, 'hard' tasks aimed at numerical computations require high-precision computing and are also accompanied by energy efficiency problems. Numerical computations exist in lots of applications, including partial differential equations (PDEs) and large-scale matrix multiplication. Therefore, it is necessary to study CIM for numerical computations. This article reviews the recent developments of CIM for numerical computations. The different kinds of numerical methods solving partial differential equations and the transformation of matrixes are deduced in detail. This paper also discusses the iterative computation of a large-scale matrix, which tremendously affects the efficiency of numerical computations. The working procedure of the ReRAM-based partial differential equation solver is emphatically introduced. Moreover, other PDEs solvers, and other research about CIM for numerical computations, are also summarized. Finally, prospects and the future of CIM for numerical computations with high accuracy are discussed.

Fiber-optic large-depth 3D chromatic confocal endomicroscopy.

Current endoscopy techniques have difficulties to provide both high resolution and large imaging depth, which significantly hinders the early diagnosis of gastric cancer. Here, we developed a label-free, large-depth, three-dimensional (3D) chromatic reflectance confocal endomicroscopy. In order to solve the problem of insufficient imaging depth of traditional chromatic confocal microscopy, a customized miniature objective lens both with large chromatic focal shift and correction for spherical aberration was used to focus light of different wavelengths at different depths of the sample simultaneously, and a fiber bundle containing 50000 single-mode cores was used to collect the confocal reflectance signal. To acquire detailed information along the axial direction at a faster speed, a high-speed multi-pixel spectrometer was used to realize simultaneous detection of multi-depth signals. Specifically, we have built up a label-free fiber-optic 3D chromatic reflectance confocal endomicroscopy, with 2.3 µm lateral resolution, imaging depth of 570 µm in 3D phantom and 220 µm in tissue, and 1.5 Hz 3D volumetric frame rate. We have demonstrated that the fiber-optic 3D chromatic confocal endomicroscopy can be used to image human gastric tissues ex vivo, and provide important morphological information for diagnosis without labeling. These results show the great potential of the fiber-optic 3D chromatic confocal endomicroscopy for gastric cancer diagnosis.

Engineering Spiking Neurons Using Threshold Switching Devices for High-Efficient Neuromorphic Computing.

Inspired by the human brain, the spike-based neuromorphic system has attracted strong research enthusiasm because of the high energy efficiency and powerful computational capability, in which the spiking neurons and plastic synapses are two fundamental building blocks. Recently, two-terminal threshold switching (TS) devices have been regarded as promising candidates for building spiking neurons in hardware. However, how circuit parameters affect the spiking behavior of TS-based neurons is still an open question. Here, based on a leaky integrate-and-fire (LIF) neuron circuit, we systematically study the effect of both the extrinsic and intrinsic factors of NbO x -based TS neurons on their spiking behaviors. The extrinsic influence factors contain input intensities, connected synaptic weights, and parallel capacitances. To illustrate the effect of intrinsic factors, including the threshold voltage, holding voltage, and high/low resistance states of NbO x devices, we propose an empirical model of the fabricated NbO x devices, fitting well with the experimental results. The results indicate that with enhancing the input intensity, the spiking frequency increases first then decreases after reaching a peak value. Except for the connected synaptic weights, all other parameters can modulate the spiking peak frequency under high enough input intensity. Also, the relationship between energy consumption per spike and frequency of the neuron cell is further studied, leading guidance to design neuron circuits in a system to obtain the lowest energy consumption. At last, to demonstrate the practical applications of TS-based neurons, we construct a spiking neural network (SNN) to control the cart-pole using reinforcement learning, obtaining a reward score up to 450. This work provides valuable guidance on building compact LIF neurons based on TS devices and further bolsters the construction of high-efficiency neuromorphic systems.