mpw-008 / NWM N-Well*

the NWM shapes of all of mpw-008

40 of 40 dies rendered.

31,545,799 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (24.5 MB in total, largest 1.2 MB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-008_NWM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-008_NWM_reticle.png
the stitched reticle, full resolution
20.2 MB 198e674c26d08433d4bd0cc75eb065cc53739c10885eaacdf58238269eb40d60951ee0e8 859be227f2ff509835bc2d3ef148b45fcbc60e5d8fa2f82aed54afb8ddc39389
mpw-008_NWM_reticle_overview.png
the same image scaled to fit 4096 px
1.9 MB 347b197aee65a12bceb81977a73125e71cbdb8593e8ea63503fb608a98a642a09ba1d536 cfaa4a6fed70d8b895eb7f651befa5cc8c30cd9ce1550a3ce8d6e530319c0f7b
mpw-008_NWM_tiles.json
where every tile belongs, and its checksum
13.4 kB efac1ca82cb7679dd4f417097389f58d0a5238c5a70b987c5ef1b4180f9af410c1a2f38f 0c8564f801ef0026b7e1f6dabf1a289e9b5ff7bdf4e76ee1af1f2057b73c7258
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 wishbone_pio 7576×107760, 0 528.4 kBbc917c44ea85d126da94d5d097f88e3a2ca73acb19eba6d8197ee50ac3a660e0
A2 iit_indore_mm 7576×107767576, 0 526.5 kBb440d6c05ebf5f62e5afec685fb2b744ff6b79bfe77e9fa1769b959ceab9d158
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 480.4 kB00d4970e743e5e736992917738fd732b9527d21978be0fec2b3e56f065e3fa09
A4 riscduino-dcore_d4_ 7576×1077622728, 0 1.1 MB26e910cccbfb6797847222e054f34dfa41b982ab3e4b683f1dbe6a5108f15d64
A5 riscduino-qcore_q3_ 7576×1077630304, 0 956.6 kB7a72801d608d2fa393f123f177c33bdd578110710b251510368d5b3da0e089ee
A6 caravel_mpw8 7576×1077637880, 0 530.2 kBaf64e137185726a30432dbf773a49f4ee434a240ae06338171687076aedc70da
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 473.9 kBd50ee98adb8c88d772c54c080d29071074433b0f9c5e3fad7738e2ca784ccbe2
B2 sramtestvehicleg4 7576×107767576, 10776 453.6 kB52e7debc7802fc565cd32988e145466f1900c3489e7f1809b02d16bd35d401e3
B3 reram_crossbar_rerun 7576×1077615152, 10776 481.8 kB1672d04ad776a45bd5331093a9fe0b9c3b69e2ddb5fdffb0186a0c835555c1dd
B4 tinytapeout_skullart 7576×1077622728, 10776 763.5 kBc7441c08f0104ddf9c4b806a3af22e8e61608e543529376fde7a99e376e8a9ec
B5 soomrv__mpw-8_ 7576×1077630304, 10776 1.2 MB7b4625c580cf1d687c8e8d38940b771e01ff4ba6a9d11ab039ae4a20065e40c5
B6 clock_divide_selector 7576×1077637880, 10776 480.2 kBa31b771078e477abf564d3af17e8cd0e86b38786a6e1ad4b963ee5cc635cb2a2
B7 reram_module_mpw8 7976×1077645456, 10776 433.4 kB0f56e7cfa5ddb0ed328cd46f6aa6a0f1903f2558c1e906917bf6f877a8604a93
C1 shapingthewaves_8 7576×107760, 21552 896.0 kBacdc51a82cc3bc4ff81c7015844cef049ae35e8e70dd938810971498535a1725
C2 leosoc 7576×107767576, 21552 648.7 kB4d88a8d1ce17fd5d853cc0ae602e13f6bc416a31beabf30b11161db469ec15d1
C3 decrypt_aes128 7576×1077615152, 21552 705.7 kB4677e14825f49e0f746f3e626a57e000960b88c028ee11e9c4175c135711b8c1
C4 approximate_adder_axha 7576×1077622728, 21552 482.0 kB4e3c38a96beabeed80a583bb88312481d31c5ff2daeb88b0e885a49afcbe644e
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 616.3 kB1163913b96a3a97bc9cc264fcd05a1dcf45ed72768044693d6de55311769ae15
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 485.5 kB09b6adc62da7239448d81fa25e51d73d27dc8e68ea16c90cadd3d353bd06656a
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 1.1 MB3938c3f33ea552bbde3c89f5229329e195f45b983d8f6cb4ab660375c0858033
D2 hyperram_controller_for_sky130 7576×107767576, 32328 482.7 kB84fa96645c16dcaad990ce808fa88b8faf2caff0f8d48a6a5e0dc948767710f3
D3 hardened_alu 7576×1077615152, 32328 507.2 kB280b998bbe9fa189fd0bcecf6333de5788bde297763978f48a5298466ae77422
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 834.7 kB4d29f14fcb02993195be3a65458602cc676a179e109aef7985cf1177bad094f2
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 466.7 kB54efcee22459633a644239f379130dd39bc6ae493eaa11f6042036b972f490db
D6 gf180_vs_sky130 7576×1077637880, 32328 641.0 kBbc4bd5e7b10116db902bb3a0777e86e5e7392aa8118494719d58748755bd17c6
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 455.0 kBf37ffee68efe7d8b0a63d7ad0965fa00fe0bd000bf91d705ed3fc5b71b1d5a54
E1 iiitb_sd_mpw8 7576×107760, 43104 470.7 kBb1d39d4e20e5e046f686f8b53e6e54616e37dd25fe6a6b0abbc7378089a81411
E2 iiitb_sfifo 7576×107767576, 43104 463.7 kB1da04f332db7e0ca855f6411175976402e1422b5e90137a1084913c93ceb4124
E3 iiitb_alu 7576×1077615152, 43104 456.9 kBb9db972585159f6f18e7e781bd34f3fdc5c0abf24fb9042f4705813c099d0408
E4 ppcpu2 7576×1077622728, 43104 1.0 MB88b489e572dbb5737f77f2a25d0dbe23cf848bd75e92e9c4977da4691fd2bf4a
E5 mega4_reram 7576×1077630304, 43104 479.7 kBaaae71bf44bef8c50d685b86f08f0c06dca80d72a06a6c61ae18a8fca8296008
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 516.2 kB0e0a999208b94035fca977c1e9d4514927efd88c52e07a11dd124a8775d935f3
E7 sky130cla 7976×1077645456, 43104 446.1 kB912d2e9376273de57383495e9256b1afe26420c186f030e48a39a558586a8e97
F1 uetrv_esoc_v2 7576×111760, 53880 631.9 kB81daf0d5a7b8e0b46f2f615fc9b265225fb2873acc5fdc65a38d39fa2ef4f570
F2 sequencedetection1010 7576×111767576, 53880 454.0 kB9bb41cc79045dcc8b2008d3ebb015478ddd049d0929c01b27b5767fa934e6b1d
F3 testasynctrimux-mpw8 7576×1117615152, 53880 498.4 kB284b973f1ddbaf7b55b7ad751b0da14db88da9068b66646bf55aa0cd2037ee55
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 505.3 kB98db069ba0e42a3cad043d929274e256072627c66cb60b94302482af0830448a
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 479.6 kB925d97f0ddea7ef48b74f1e1d24c5cb4738b5e16ce3d80ba4f48436d7cf850d8
F6 arbiter_puf 7576×1117637880, 53880 469.5 kB1b2fb625fb99700d6fb475478ab0779209202e1fd9d5260c499cec25ff23e9b5
F7 unigate-sky 7976×1117645456, 53880 743.6 kBe9212d4e16c03af7ae704fe3a4afb84edf1dd6634ec6506e320f837e07f3cfb8

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About NWM on mpw-008

Resolved: reticle 5CS8017AC, run mpw-008, mask NWM

Mask layer
NWM — N-Well*
Process step
17 — N-well mask
Drawn layer
64:20
Layer on the mask-layer GDS
21:0
Detail
NWM = nwell sized (cnwm.3a outside hvi / 3b inside), with DEPMOS/pwell-resistor exemptions
Used in SKY130
yes

mpw-008 — SKY130 MPW-8

Reticle set
5CS8017AC
Run date
2022-11-19
Submissions closed
2022-12-31
Projects
144
Identification
inferred — Sequence after 8016=MPW-7 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-8 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-008/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.