learning, reinforcement learning (RL) is one of machine learning paradigms, which allow the machine to interact with the environment and update the policy according to the negative or positive reward signals. [10,11] The algorithms of RL [10] mainly include artificial neural network (ANN)-based deep Q-learning (DQN) and spiking neural network (SNN)based reward-modulated spike-timingdependent plasticity (R-STDP). Compared with ANN-based DQN that adopts error backpropagation and gradient descent to update the weight, R-STDP updates the weight by incorporating the brain-inspired STDP with reward signals being biologically more plausible. [12][13][14][15][16] The hardware implementation of RL paradigm with DQN based on emerging nonvolatile memory technologies (NVMs) has been reported recently. [17][18][19] However, hardware realization of R-STDP based on NVMs is far more less explored. The R-STDP learning rule mainly relies on the modulation of the "standard" STDP by a reward term (positive or negative reward), which is from the external environment, thus realizing SNN-based reinforcement learning. R-STDP stores the traces of synapses that are eligible for STDP (eligibility traces), and applies the modulated weight changes at the time of receiving a positive/negative reward signal. Eligibility traces are a critical ingredient of R-STDP learning rules. [20] There have been some works realizing long-lasting eligibility traces with complementary metal-oxide-semiconductor (CMOS) technology combining large capacitors. [21][22][23][24][25] Recently, Demirağ et al. exploit the drift behavior of phase change memory devices to intrinsically perform eligibility traces with long timescales realizing higher area efficiency than the CMOS ones. [26] In addition to realize eligibility traces constructing STDP, the polarity of STDP will be further changed according to the sign of external reward signal. Thus, it requires emerging nonvolatile devices with reconfigurable characteristics for both STDP and anti-STDP.2D semiconductor field-effect (FE) transistors (FETs) with atomic-level thickness show excellent electrostatic tunability, [27][28][29][30][31][32][33] which provides the possibility of modulating the channel to be reconfigurable p-type or n-type for both STDP and anti-STDP learning rules. On the other hand, to realize nonvolatile memory characteristics, we adopt the ferroelectric poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) as gate dielectric for dynamically tunable memory Reward-modulated spike-timing-dependent plasticity (R-STDP) is a braininspired reinforcement learning (RL) rule, exhibiting potential for decisionmaking tasks and artificial general intelligence. However, the hardware implementation of the reward-modulation process in R-STDP usually requires complicated Si complementary metal-oxide-semiconductor (CMOS) circuit design that causes high power consumption and large footprint. Here, a design with two synaptic transistors (2T) connected in a parallel structure is experimentally demonstrated. The 2T...

Boron nitride (BN) has attracted substantial attention in the fields of vacuum-ultraviolet (VUV) photodetection owing to its ultra-wide bandgap and high optical absorption coefficient. However, in practical application, boron nitride crystals cannot satisfy current requirements in size and quality. In this work, we prepared an amorphous sp2 bonding BN film by magnetron sputtering with boron as the growth source at a low temperature (500 °C). No harsh conditions of high temperature and pressure are required, but the purity and uniformity of the film can be ensured by this method. Based on such a film, a VUV photodetector (PD) with metal–semiconductor–metal (MSM) structure is further constructed, which exhibits an extremely low dark current (∼10−14 A), a high photo-to-dark ratio (∼103), and an excellent spectrum selectivity of VUV band. The improvement of PD's performance benefits from the pure and compact composition of the grown BN film. These results indicate that the growing amorphous BN film at a low temperature by reactive magnetron sputtering is a feasible method for preparing high-performance BN VUV photodetectors.

With reference to the organization of the human brain nervous system, a hardware-based approach that builds massively parallel neuromorphic circuits is of great significance to neuromorphic computing. The Bienenstock–Cooper–Munro (BCM) learning rule, which describes that the synaptic weight modulation exhibits frequency-dependent and tunable frequency threshold characteristics, is more compatible with the working principle of neuromorphic computing systems than spike-timing-dependent plasticity. Therefore, it is interesting to simulate the BCM learning rule on solid-state synaptic devices. Here, we have prepared λ-carrageenan (λ-car) electrolyte-gated oxide synaptic transistors, which exhibit good transistor performances, including a low subthreshold swing of 125 mV/dec, an on/off ratio larger than 106, and a mobility of 9.5 cm2 V–1 s–1. By modulating the initial channel current and spike frequency, the simulation of the BCM rule was successfully realized. The competitive relationship between the drift of protons under an electric field and the spontaneous diffusion of protons can explain this mechanism. The proposed λ-car-gated synaptic transistor has a great significance to neuromorphic computing.

Neuromorphic computing, which merges learning and memory functions, is a new computing paradigm surpassing traditional von Neumann architecture. Apart from the plasticity of artificial synapses, the simulation of neurons’ multi‐input signal integration is also of great significance to realize efficient neuromorphic computing. Since the structure of transistors and neurons is strikingly similar, capacitively coupled multi‐terminal pectin‐gated oxide electric double layer transistors are proposed here as artificial neurons for classification. In this work, the free logic switching of “AND” and “OR” is realized in the device with triple in‐plane gates. More importantly, the linear classification function on a single neuron transistor is demonstrated experimentally for the first time. All the results obtained in this work indicate that the prepared artificial neuron can improve the efficiency of artificial neural networks and thus will play an important role in neuromorphic computing.

The prevailing transmission of image information over the Internet of Things demands trustworthy cryptography for high security and privacy. State-of-the-art security modules are usually physically separated from the sensory terminals that capture images, which unavoidably exposes image information to various attacks during the transmission process. Here we develop in-sensor cryptography that enables capturing images and producing security keys in the same hardware devices. The generated key inherently binds to the captured images, which gives rise to highly trustworthy cryptography. Using the intrinsic electronic and optoelectronic characteristics of the 256 molybdenum disulfide phototransistor array, we can harvest electronic and optoelectronic binary keys with a physically unclonable function and further upgrade them into multiple-state ternary and double-binary keys, exhibiting high uniformity, uniqueness, randomness, and coding capacity. This in-sensor cryptography enables highly trustworthy image encryption to avoid passive attacks and image authentication to prevent unauthorized editions.