Semiconductor Energy Laboratory Co., Ltd.

Takahiro Fukutome

Hajime Kimura

Takayuki Ikeda

Hidenori Mori

Shunpei Yamazaki

Toru Tanabe

Kei Takahashi

Teppei Oguni

Daisuke Kurosaki

Masakatsu Ohno

Koji Moriya

Shigeru Onoya

Hironori Matsumoto

Hiroyuki Miyake

Kengo Akimoto

Koji Kusunoki

Hiromichi Godo

Takeshi Osada

Junpei Momo

Hiroki Inoue

Hitoshi Kunitake

Takeshi Nishi

Kazunori Watanabe

Toshiji Hamatani

Ryota Tajima

Susumu Kawashima

Munehiro Kozuma

Takashi Nakagawa

Seiichi Yoneda

Ryo Arasawa

Akio Endo

Kunitaka Yamamoto

Kazuaki Ohshima

Yusuke Negoro

Roh Yamamoto

Kentaro Hayashi

Takeya Hirose

Kazuki Higashi

Noboru Inoue

A small-size semiconductor device is provided. The semiconductor device includes a first layer, a second layer, and a third layer formed over a substrate. A first transistor included in the first layer includes a first semiconductor layer containing Si. A second transistor included in the second layer includes a second semiconductor layer containing Ga. A third transistor included in the third layer includes a third semiconductor layer containing at least one of In and Zn. The first semiconductor layer of the first transistor is formed using the substrate. The second semiconductor layer of the second transistor is formed using a crystal obtained by crystal growth over the substrate. The third semiconductor layer of the third transistor is formed above the first semiconductor layer and the second semiconductor layer.

Semiconductor device

A novel content classification method is provided. A content classification method using machine learning for a learning model and a classifier fabrication method are provided. In Step 1, a data set containing a plurality of contents is acquired. Learning labels are attached to m contents, and the learning labels are not attached to the remaining contents. In Step 2, a first learning model is created by machine learning using the m contents. In Step 3, judgment labels are attached to the plurality of contents using the first learning model and are displayed on a GUI. In Step 4, new learning labels are attached to k contents in the plurality of contents. In Step 5, a second learning model is created by the machine learning using the k contents. In Step 6, judgment labels are attached to the plurality of contents using the second learning model and are displayed on the GUI.

Content classification method

A parameter candidate for a semiconductor element is provided. A data set of measurement data is provided to a parameter extraction portion, and a model parameter is extracted. A first netlist is provided to a circuit simulator, simulation is performed using the first netlist and the model parameter, and a first output result is output. A classification model learns the model parameter and the first output result and classifies the model parameter. A second netlist and a model parameter are provided to the circuit simulator. A variable to be adjusted is supplied to a neural network, an action value function is output, and the variable is updated. The circuit simulator performs simulation using the second netlist and the model parameter. When a second output result to be output does not satisfy conditions, a weight coefficient of the neural network is updated. When the second output result satisfies the conditions, the variable is judged to be the best candidate.

Parameter search method

An image retrieval system that enables high-accuracy image retrieval in a short time is provided. The image retrieval system includes a processing portion provided with a neural network. The neural network includes a layer provided with a neuron. The processing portion has a function of comparing query image data with a plurality of pieces of database image data, and extracting the database image data including an area with a high degree of correspondence to the query image data as extracted image data. The processing portion has a function of extracting data of the area with a high degree of correspondence to the query image data from the extracted image data, as partial image data. The layer has a function of outputting an output value corresponding to the features of the image data input to the neural network. The processing portion has a function of comparing the above output values in the case where the respective pieces of partial image data are input with the above output value in the case where the query image data is input.

Image retrieval system and image retrieval method

A multiplier circuit includes a first circuit comprising a first transistor, a second transistor, a first capacitor, and a second capacitor. It further includes a second circuit comprising a third transistor, a fourth transistor, a third capacitor, and a fourth capacitor.

Multiplier circuit

An imaging device connected to a neural network is provided. An imaging device having a neuron in a neural network includes a plurality of first pixels, a first circuit, a second circuit, and a third circuit. Each of the plurality of first pixels includes a photoelectric conversion element. The plurality of first pixels is electrically connected to the first circuit. The first circuit is electrically connected to the second circuit. The second circuit is electrically connected to the third circuit. Each of the plurality of first pixels generates an input signal of the neuron. The first circuit, the second circuit, and the third circuit function as the neuron. The third circuit includes an interface connected to the neural network.

Imaging device, imaging module, electronic device, and imaging system

Person's conditions are classified according to his/her eye data. Person's conditions are classified according to his/her eye data using an imaging device, a feature extraction unit, and a classifier. The imaging device has a function of generating a group of images by continuous image capturing, and the group of images preferably includes an image of an eye area. The eye includes a black area and a white area. The method includes the steps in which the feature extraction unit extracts the eye area from the group of images, extracts a blinking amplitude, detects an image for determining start of eye blinking, stores an image for determining end of eye blinking as first data, and stores an image after a predetermined time elapsed from the first data as second data. The step in which the feature extraction unit extracts the white area from the first data and the second data is included. The classifier can use the white area as learning data.

Classification method

A semiconductor device that inhibits deterioration of a secondary battery is provided. The semiconductor device includes a secondary battery module and a first circuit. The secondary battery module includes a secondary battery and a sensor. The first circuit includes a variable resistor. The sensor has a function of measuring a temperature of the secondary battery. The first circuit has a function of judging the charge voltage of the secondary battery and outputting a first result; a function of judging the temperature of the secondary battery measured by the sensor and outputting a second result; a function of determining the magnitude of the variable resistor on the basis of the first result and the second result; a function of discharging the charge voltage through the variable resistor; and a function of stopping discharge when the charge voltage reaches a specified voltage.

A semiconductor device in which temperature dependence is reduced is provided. A switched capacitor is formed using a second transistor, a third transistor, and a second capacitor. Semiconductor layers of the second transistor and the third transistor that include an oxide can reduce temperature dependence. An AC signal supplied to the gates of the second transistor and the third transistor is converted into a DC voltage through the switched capacitor. Note that the level of the DC voltage is adjusted by the levels of the voltages supplied to the back gates of the second transistor and the third transistor.

Semiconductor device or oscillator

A flexible semiconductor device including a light-emitting element and a sensor element is provided. The semiconductor device includes a sensor device, a processor, and a communication device. The sensor device includes a first pixel and a second pixel formed over a flexible substrate. The first pixel includes a light-emitting element and a first transistor. The second pixel includes a sensor element having a photoelectric conversion function and a second transistor. Light emitted from the light-emitting element has a peak wavelength. A range of wavelength sensed by the sensor element includes the peak wavelength. A semiconductor layer of the first transistor and a semiconductor layer of the second transistor include the same element. A pixel electrode of the light-emitting element has a function of being electrically connected to the first transistor and a function of blocking diffusion light to the sensor element.

Sensor device and semiconductor device

Text is generated from an object. Text is generated from a first object. The first object includes a second object and a third object. A step of detecting coordinate data of the second object is included. A step of detecting coordinate data of the third object is included. A step of extracting positional relation between the second object and the third object from coordinate data is included. A step of converting the extracted positional relation into graph data is included. A step of generating text about the positional relation between the second object and the third object from graph data is included.

Object-to-text conversion method and system

An imaging device with a novel structure is provided. The imaging device includes an imaging region provided with a plurality of pixels. The plurality of pixels included in the imaging region include a first pixel and a second pixel. The imaging device has a function of selecting a first region or a second region. The first region includes the same number of pixels as the second region. The first region includes at least the first and second pixels. The second region includes at least the second pixel. The pixels included in the first region or the second region have a function of outputting imaging signals obtained by the pixels. The imaging device generates first image data by concurrently reading the imaging signals output from the pixels included in the first region and performing arithmetic operation on the signals. The imaging device generates second image data by concurrently reading the imaging signals output from the pixels included in the second region and performing arithmetic operation on the signals. A first conceptual image can be generated with the use of the first image data and the second image data.

Imaging device or imaging system

A novel display device where a light-emitting element is turned on by a triangle wave is provided. One embodiment of the present invention is a method for driving a display device including a first pixel, a second pixel, a first wiring, a second wiring, and a third wiring. The first wiring is electrically connected to the first pixel and the second pixel. The second wiring and the third wiring are electrically connected to the first pixel and the second pixel, respectively. At a first time, the first pixel reaches the maximum luminance corresponding to first display data and the second pixel reaches the maximum luminance corresponding to second display data. The first pixel and the second pixel are initialized at a second time different from the first time by input of a reset signal to the first wiring to stop light emission.

Display device and method for driving display device

A semiconductor device including a multiplier circuit is provided. A first cell, a second cell, and a first circuit are included. The first cell includes a first transistor. The second cell includes a second transistor. The first circuit includes a third transistor, a fourth transistor, a fifth transistor, a sixth transistor, a first capacitor, a second capacitor, and a first switch.

Growing community of inventors

Kanagawa, Japan

Takahiro Fukutome