JP7128630B2 - 表示システム - Google Patents
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- JP7128630B2 JP7128630B2 JP2018023972A JP2018023972A JP7128630B2 JP 7128630 B2 JP7128630 B2 JP 7128630B2 JP 2018023972 A JP2018023972 A JP 2018023972A JP 2018023972 A JP2018023972 A JP 2018023972A JP 7128630 B2 JP7128630 B2 JP 7128630B2
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Description
本実施の形態では、本発明の一態様に係る半導体装置及び表示システムについて説明する。
図1に、表示システム10の構成例を示す。表示システム10は、外部から受信したデータに基づいて映像を表示するための信号を生成し、当該信号に基づいて映像を表示する機能を有する。表示システム10は、表示部20、信号生成部30、演算部40を有する。表示部20及び信号生成部30は、表示装置11によって構成することができる。また、演算部40は、演算装置によって構成することができる。
表示部20は、信号生成部30から入力される信号に基づいて、映像を表示する機能を有する。表示部20は、画素部21、駆動回路22、及び駆動回路23を有する。
図1に示す信号生成部30は、外部から入力された信号に基づいて映像信号を生成する機能を有する。信号生成部30は、受信部31、処理部32、処理部33、及び処理部34を有する。
演算部40は、ニューラルネットワークの学習を行う機能を有する。演算部40としては、専用サーバやクラウドなどの演算処理能力の優れた演算装置を用いることができる。演算部40は、データベース41、処理部42、及び処理部43を有する。なお、データベース41は演算部40の外部に設けられていてもよい。
次に、学習機能を有するニューラルネットワークの構成例について説明する。ニューラルネットワークNNの構成例を、図4に示す。ニューラルネットワークNNは、ニューロン回路と、ニューロン回路間に設けられたシナプス回路によって構成される。
次に、表示システム10の動作例について説明する。図5は、ニューラルネットワークの学習を行う際の動作例を示すフローチャートである。図6は、ニューラルネットワークの推論により階調の補正を行う際の動作例を示すフローチャートである。
ニューラルネットワークの学習について、図5を用いて説明する。まず、演算部40において、データベース41からデータX及びデータTが読み出される(ステップS1)。前述の通り、データXは表示部20に実際に表示された映像に対応するデータであり、データTは表示部20への表示を意図している理想的な映像に対応するデータである。そして、処理部42において、データX及びデータTがそれぞれN×Mのデータに分割される(ステップS2)。これにより、N×MのデータXdivとN×MのデータTdivが生成される(ステップS3)。
次に、ニューラルネットワークの推論について、図6を用いて説明する。まず、上記の学習によって得られたN×Mの重み係数Wが、ニューラルネットワークNN1に格納される(ステップS11)。これにより、処理部33に階調のばらつきを補償する機能が付加される。
本実施の形態では、上記実施の形態で説明したニューラルネットワークに用いることができる半導体装置の構成例について説明する。
半導体装置100の構成の一例を図7に示す。図7に示す半導体装置100は、記憶回路110(MEM)と、参照用記憶回路120(RMEM)と、回路130と、回路140と、を有する。半導体装置100は、さらに電流源回路150(CREF)を有していても良い。
=Σi{k(Vw[i]-Vth+VPR)2-k(Vw[i]-Vth+VPR-Vx[i、j])2}
=2kΣi(Vw[i]・Vx[i、j])-2kΣi(Vth-VPR)・Vx[i、j]-kΣiVx[i、j]2 (式4)
次いで、記憶回路110(MEM)と、参照用記憶回路120(RMEM)の具体的な構成の一例について、図8を用いて説明する。
次いで、回路130と、回路140と、電流源回路150(CREF)の具体的な構成の一例について、図10を用いて説明する。
次いで、図9~図11を用いて、本発明の一態様に係る半導体装置100の具体的な動作の一例について説明する。
本実施の形態では、上記実施の形態で説明した表示部の他の構成例について説明する。
本実施の形態では、上記実施の形態で説明した表示システムに用いることができる表示装置の構成例について説明する。
本実施の形態では、上記実施の形態において用いることができるOSトランジスタの構成例について説明する。
図15(A)は、トランジスタの構成例を示す上面図である。図15(B)は、図15(A)のX1-X2線断面図であり、図15(C)はY1-Y2線断面図である。ここでは、X1-X2線の方向をチャネル長方向と、Y1-Y2線方向をチャネル幅方向と呼称する場合がある。図15(B)は、トランジスタのチャネル長方向の断面構造を示す図であり、図15(C)は、トランジスタのチャネル幅方向の断面構造を示す図である。なお、デバイス構造を明確にするため、図15(A)では、一部の構成要素が省略されている。
次に、OSトランジスタと他のトランジスタの積層によって構成される半導体装置の構造について説明する。
本実施の形態では、上記実施の形態で説明したトランジスタに用いることができる、金属酸化物について説明する。以下では特に、金属酸化物とCAC(Cloud-Aligned Composite)-OSの詳細について説明する。
本実施の形態では、本発明の一態様の電子機器について、図面を参照して説明する。
11 表示装置
20 表示部
21 画素部
22 駆動回路
23 駆動回路
24 画素
25 領域
30 信号生成部
31 受信部
32 処理部
33 処理部
34 処理部
40 演算部
41 データベース
42 処理部
43 処理部
100 半導体装置
110 記憶回路
120 参照用記憶回路
130 回路
140 回路
150 電流源回路
300 表示装置
301 基板
302 絶縁層
303 絶縁層
304 配線
305 絶縁層
306 絶縁層
307 絶縁層
308 電極
309 FPC
310 異方性導電層
311 シール材
312 基板
331 電極
332 半導体層
333 電極
334 電極
335 電極
336 電極
341 電極層
342 発光層
343 電極層
344 隔壁
345 充填材
801 トランジスタ
811 絶縁層
812 絶縁層
813 絶縁層
814 絶縁層
815 絶縁層
816 絶縁層
817 絶縁層
818 絶縁層
819 絶縁層
820 絶縁層
821 金属酸化物膜
822 金属酸化物膜
823 金属酸化物膜
824 金属酸化物膜
830 酸化物層
850 導電層
851 導電層
852 導電層
853 導電層
860 半導体装置
870 単結晶シリコンウエハ
871 CMOS層
872 トランジスタ層
873 ゲート電極
874 電極
875 電極
7000 表示部
7100 テレビジョン装置
7101 筐体
7103 スタンド
7111 リモコン操作機
7200 ノート型パーソナルコンピュータ
7211 筐体
7212 キーボード
7213 ポインティングデバイス
7214 外部接続ポート
7300 デジタルサイネージ
7301 筐体
7303 スピーカ
7311 情報端末機
7400 デジタルサイネージ
7401 柱
7411 情報端末機
Claims (5)
- 演算部と、信号生成部と、を有し、
前記演算部は、データベースと、第1の処理部と、第2の処理部と、を有し、
前記データベースは、第1のデータと、第2のデータと、を記憶する機能を有し、
前記第1のデータは、N行M列(N、Mは2以上の整数)の領域に分割された画素部を有する表示部に表示された映像に対応するデータであり、
前記第2のデータは、前記表示部への表示が意図された映像に対応するデータであり、
前記第1の処理部は、前記第1のデータを、N×Mの第3のデータに分割する機能を有し、
前記第1の処理部は、前記第2のデータを、N×Mの第4のデータに分割する機能を有し、
前記信号生成部は、受信部と、第3の処理部と、第4の処理部と、第5の処理部と、を有し、
前記受信部は、画像データを受信する機能を有し、
前記第3の処理部は、前記画像データを、N×Mの第5のデータに分割する機能を有し、
前記第4の処理部は、N×Mの前記第5のデータを補正する機能を有し、
前記第5の処理部は、補正されたN×Mの前記第5のデータを結合して映像信号を生成する機能を有し、
前記第2の処理部は、第1のニューラルネットワークを有し、
前記第4の処理部は、第2のニューラルネットワークを有し、
前記第1のニューラルネットワークは、前記第3のデータ及び前記第3のデータに対応する前記第4のデータを用いて学習を行う機能を有し、
前記学習によって得られたN×Mの重み係数が、前記第2のニューラルネットワークに出力され、
前記第2のニューラルネットワークは、推論によってN×Mの前記第5のデータの補正を並列処理する機能を有する表示システム。 - 請求項1において、
前記第1のニューラルネットワークは、前記第3のデータを学習データ、前記第4のデータを教師データとして用いて前記学習を行う機能を有する表示システム。 - 請求項1又は2において、
前記第1のデータは、前記表示部に表示された映像を撮像することによって取得されたデータである表示システム。 - 請求項1乃至請求項3のいずれか一において、
前記第2のニューラルネットワークは、積和演算素子を有し、
前記積和演算素子は、第1のトランジスタと、第2のトランジスタと、容量素子と、を有する記憶回路を有し、
前記第1のトランジスタのソース又はドレインの一方は、前記第2のトランジスタのゲート及び前記容量素子と電気的に接続され、
前記第1のトランジスタは、チャネル形成領域に金属酸化物を有する表示システム。 - 請求項1乃至請求項4のいずれか一において、
前記画素部は複数の画素を有し、
前記画素は発光素子を有する表示システム。
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US11507818B2 (en) | 2018-06-05 | 2022-11-22 | Lightelligence PTE. Ltd. | Optoelectronic computing systems |
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US11515873B2 (en) | 2018-06-29 | 2022-11-29 | Semiconductor Energy Laboratory Co., Ltd. | Semiconductor device and electronic device |
US10924090B2 (en) * | 2018-07-20 | 2021-02-16 | Semiconductor Energy Laboratory Co., Ltd. | Semiconductor device comprising holding units |
US11170481B2 (en) * | 2018-08-14 | 2021-11-09 | Etron Technology, Inc. | Digital filter for filtering signals |
JP7126412B2 (ja) * | 2018-09-12 | 2022-08-26 | 東京エレクトロン株式会社 | 学習装置、推論装置及び学習済みモデル |
WO2020075719A1 (ja) * | 2018-10-10 | 2020-04-16 | キヤノン株式会社 | 画像処理装置、画像処理方法及びプログラム |
JP7250653B2 (ja) | 2018-10-10 | 2023-04-03 | キヤノン株式会社 | 画像処理装置、画像処理方法及びプログラム |
US11734556B2 (en) | 2019-01-14 | 2023-08-22 | Lightelligence PTE. Ltd. | Optoelectronic computing systems |
TWI741533B (zh) * | 2019-03-19 | 2021-10-01 | 美商光子智能股份有限公司 | 計算系統、計算裝置及計算系統的操作方法 |
JP7050028B2 (ja) * | 2019-03-28 | 2022-04-07 | 株式会社日立製作所 | 計算機システム及び機械学習の制御方法 |
WO2020217140A1 (ja) | 2019-04-26 | 2020-10-29 | 株式会社半導体エネルギー研究所 | 文書検索システム、および文書検索方法 |
CN110322853B (zh) * | 2019-06-19 | 2020-08-11 | 电子科技大学 | 一种基于神经网络的智能显示驱动系统 |
WO2021028755A1 (ja) * | 2019-08-09 | 2021-02-18 | 株式会社半導体エネルギー研究所 | 表示装置の動作方法 |
US12025862B2 (en) | 2019-12-04 | 2024-07-02 | Lightelligence PTE. Ltd. | Optical modulation for optoelectronic processing |
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