RAM board

cherry-takuan·nlp·nlp-16a/Hardware/RAM_board/RAM_board.kicad_pcb

RAM board layout, top view — open source board by cherry-takuan

About the RAM board

RAM board is an open source PCB design by cherry-takuan, published on GitHub under the CC-BY-SA-4.0 license. It is a 2-layer board measuring 99.7 × 99.7 mm, with 19 components from 10 distinct parts.

Other key parts include the TC551001 and 74LS32. By type, the board carries 6 connectors, 4 capacitors, 3 ICs, 3 resistors, 2 diodes and 1 switch.

From the project

NLP(Nand Logic Processor)のリポジトリ

ALU制御回路回路はALUを操作するための制御信号生成し,また演算結果からフラグを生成する機能を持っています。 #### ALU本体 aluc

最新バージョン : v1.1 *** ### レジスタファイル レジスタファイルとは,プロセッサ内で一時的に記憶するレジスタと呼ばれる回路を複数集積したものです。NLP-16Aでは16bitのデータを記憶可能なレジスタを16本持っています。

汎用レジスタは5本で,残りのレジスタは何らかの役割を持ち,単純な記憶以外の動作も行うことがあります。

最新バージョン : v1.2 *** ### 入出力 NLP-16Aは16 bit x 64 kwordの1つの入出力ポートを持っています。 内部的にはレジスタの一つとしてアクセス可能な構造になっています。

主記憶及び外部デバイスとの接続に使用されます。 #### 外部バス制御 Busterminator

Main components on the RAM board

RAM board bill of materials (BOM)

19 components, 10 distinct parts.

QtyPartRefs
2
TC551001
U1, U2
1
74LS32
U3
3
Conn_02x10_Odd_Even
J1, J3, J5
2
Conn_02x20_Odd_Even
J2, J4
1
Screw_Terminal_01x02
J6
1
SW_Push
SW1
2
LED
D1, D2
1
C_Polarized
C1
3
C
C2, C3, C4
3
R
R1, R2, R3

RAM board design files

The KiCad project lives in the cherry-takuan/nlp repository on GitHub; these links point at the commit this page was built from.

RAM board: common questions

How big is the RAM board?

The RAM board measures 99.7 × 99.7 mm, has 2 copper layers and is 1.6 mm thick.

How many components are on the RAM board?

19 components, from 10 distinct parts. The full bill of materials is listed on this page.

Where can I download the RAM board design files?

From the cherry-takuan/nlp repository on GitHub, which has the KiCad layout and the schematic; the links under Design files point to each one.

Can I use the RAM board design in my own project?

Yes, under the terms of its CC-BY-SA-4.0 license, which cherry-takuan chose for the repository.

More boards in nlp