National Yang Ming Chiao Tung University

Taiwan Semiconductor Manufacturing Comp. Ltd.

Industrial Technology Research Institute

Winbond Electronics Corporation

Cornell University

Tuo-Hung Hou

Chih-Sheng Lin

Jian-Wei Su

Fu-Cheng Tsai

Samuel C Pan

Chung-Wei Hsu

Yu-Hui Lin

Pang-Shiuan Liu

Shih-Chang Chen

Liang-Gi Yao

Heng-Yuan Lee

Ming-Fang Wang

Sih-Han Li

Yeou-Ming Lin

I-Ting Wang

Chih-Cheng Chang

Chun-Tse Chou

Chih-Wei Yang

Shyh-Shyuan Sheu

Che-Chia Chang

Chi-Chun Chen

Chia-Lin Chen

Edwin Chihchuan Kan

Kuo-Hua Tseng

Jeng-Hua Wei

Shih-Chieh Wu

Ming-Hung Wu

Chih-Ming Lai

Wei-Li Lai

Jen-Chieh Liu

Jyun-Hong Huang

Boris Hudec

Mei-Chin Chen

Kai-Lin Mai

Disclosed is a memory cell including a first transistor having a first terminal coupled to a bit line; a second transistor having a first terminal coupled to a bit line bar; a weight storage circuit coupled between a gate terminal of the first transistor and a gate terminal of the second transistor, storing a weight value, and determining to turn on the first transistor or the second transistor according to the weight value; and a driving circuit coupled to a second terminal of the first transistor, a second terminal of the second transistor, and at least one word line, receiving at least one threshold voltage and at least one input data from the word line, and determining whether to generate an operation current on a path of the turned-on first transistor or the turned-on second transistor according to the threshold voltage and the input data.

Memory cell

A metallic ferroelectric metal (MFM) field effect transistor (FET) is provided that includes an MFM, a first FET and a second FET. The MFM has a first electrode. The first FET is electrically connected to the first electrode, and has a first gate electrode, wherein the first gate electrode has a first area. The second FET is electrically connected to the first electrode, and has a second gate electrode, wherein the second gate electrode has a second area, and the first area and the second area have a ratio therebetween ranging from 1:50 to 1:2.

MFMFET, MFMFET array, and the operating method thereof

According to an exemplary embodiments, the disclosure is directed to a memory circuit which includes not limited to a first half sense amplifier circuit connected to a first plurality of memory cells through a first bit line and configured to receive a unit of analog electrical signal from each of the first plurality of memory cells and to generate a first half sense amplifier output signal corresponding to the first bit line based on a first gain of the half sense amplifier and an accumulation of the units of analog signals, a locking code register circuit configured to receive a locking data and to generate a digital locking sequence, and a source selector circuit configured to receive the digital locking sequence and to generate a first adjustment signal to adjust the first half sense amplifier output signal corresponding to the first bit line by adjusting the first gain.

Memory circuit with sense amplifier calibration mechanism

A computing in memory (CIM) cell includes a memory cell circuit, a first semiconductor element, a second semiconductor element, a third semiconductor element, and a fourth semiconductor element. A first terminal of the first semiconductor element receives a bias voltage. A control terminal of the first semiconductor element is coupled to a computing word-line. A control terminal of the second semiconductor element is coupled to a first data node in the memory cell circuit. A second terminal of the third semiconductor element is adapted to receive a reference voltage. A control terminal of the third semiconductor element receives an inverted signal of the computing word-line. A first terminal of the fourth semiconductor element is coupled to a first computing bit-line. A second terminal of the fourth semiconductor element is coupled to a second computing bit-line.

Computing in memory cell

A neuron circuit and an artificial neural network chip are provided. The neuron circuit includes a memristor and an integrator. The memristor generates a pulse train having an oscillation frequency when an applied voltage exceeds a predetermined threshold. The integrator is connected in parallel to the memristor for receiving and accumulating input pulses transmitted by a previous layer network at different times, and driving the memristor to transmit the pulse train to a next layer network when a voltage of the accumulated input pulses exceeds the predetermined threshold.

Neuron circuit and artificial neural network chip

A computing in memory (CIM) cell includes a memory cell circuit, a first semiconductor element, a second semiconductor element, and a third semiconductor element. A first terminal of the first semiconductor element is coupled to a first computing bit-line. A control terminal of the first semiconductor element is coupled to a computing word-line. A control terminal of the second semiconductor element is coupled to the memory cell circuit. A first terminal of the second semiconductor element is coupled to a second terminal of the first semiconductor element. A first terminal of the third semiconductor element is coupled to a second terminal of the second semiconductor element. A second terminal of the third semiconductor element is coupled to a second computing bit-line. A control terminal of the third semiconductor element receives a bias voltage.

A device includes first wires, second wires, resistors, and a processor. Input signals are transmitted from the first wires through the resistors to the second wires. The processor receives a sum value of the input signals from one of the second wires, and shifts the sum value by a nonlinear activation function to generate a shifted sum value. The processor calculates a backpropagation value based on the shifted sum value and a target value, and generates a pulse number based on a corresponding input signal of the input signal and the backpropagation value. Each of a value of the corresponding input signal and the backpropagation value is higher than or equal to a threshold value. The processor applies a voltage pulse to one of the resistors related to the corresponding input signal based on the pulse number.

Device and method for operating the same

A computation operator in memory and an operation method thereof are provided. The computation operator in memory includes a word line calculator, a decision-maker and a sense amplifier. The word line calculator calculates a number of enabled word lines of a memory. The decision-maker generates a plurality of reference signals according to at least one of the number of enabled word lines and a used size of the memory, the reference signals are configured to set a distribution range. The sense amplifier receives a readout signal of the memory, and obtains a computation result by converting the readout signal according to the reference signals.

Computation operator in memory and operation method thereof

A semiconductor device including a field effect transistor (FET) device includes a substrate and a channel structure formed of a two-dimensional (D) material over the substrate. Source and drain contacts are formed partially over theD material. A first dielectric layer is formed at least partially over the channel structure and at least partially over the source and drain contacts. The first dielectric layer is configured to trap charge carriers. A second dielectric layer is formed over the first dielectric layer, and a gate electrode is formed over the second dielectric layer.

Impact ionization semiconductor device and manufacturing method thereof

A neural network processing system includes at least one synapse and a neuron circuit. The synapse receives an input signal and has an external weighted value and an internal weighted value, and the internal weighted value has a variation caused by an external stimulus. When the variation of the internal weighted value accumulates to a threshold value, the external weighted value varies and the input signal is multiplied by the external weighted value of the synapse to generate a weighted signal. A neuron circuit is connected with the synapse to receive the weighted signal transmitted by the synapse, and calculates and outputs the weighted signal. The present invention can simultaneously accelerate the prediction and learning functions of the deep learning and realize a hardware neural network with high precision and real-time learning.

Neural network processing system

A semiconductor device including a field effect transistor (FET) device includes a substrate and a channel structure formed of a two-dimensional (2D) material over the substrate. Source and drain contacts are formed partially over the 2D material. A first dielectric layer is formed at least partially over the channel structure and at least partially over the source and drain contacts. The first dielectric layer is configured to trap charge carriers. A second dielectric layer is formed over the first dielectric layer, and a gate electrode is formed over the second dielectric layer.

A fabrication method for two-dimensional materials of the present invention includes the following steps: forming a thin film having at least one two-dimensional element on a substrate; forming at least one capping layer on the thin film; annealing the thin film to form a two-dimensional material film after the capping layer is formed.

Fabrication method for two-dimensional materials

A resistive random access memory (RRAM) including a first electrode, a second electrode, and a charge trapping layer is provided. The second electrode is located on the first electrode. The charge trapping layer is located between the first electrode and the second electrode. The charge trapping includes a first region and a second region. The first region has a first dopant and is close to the first electrode. The second region has a second dopant and is close to the second electrode.

Resistive random access memory having charge trapping layer, manufacturing method thereof, and operation thereof

The present disclosure provides a self-rectifying RRAM cell structure including a first electrode layer formed of a nitride of a first metal element, a second electrode layer formed of a second metal element that is different from the first metal element, a first resistive switching layer and a second resistive switching layer. The first resistive switching layer is sandwiched between the first electrode layer and the second resistive switching layer, and the second resistive switching layer is sandwiched between the first resistive switching layer and the second electrode layer. The first resistive switching layer has a first bandgap that is lower than the second bandgap of the second resistive switching layer. Furthermore, a RRAM 3D crossbar array architecture is also provided.

Growing community of inventors

Hsinchu, Taiwan

Tuo-Hung Hou