mpw-002 / NPCM Nitride Poly Cut

the NPCM shapes of all of mpw-002

40 of 40 dies rendered.

21,057,403 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 (88.7 MB in total, largest 6.0 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-002_NPCM_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-002_NPCM_reticle.png
the stitched reticle, full resolution
56.3 MB 67746c857ce7f79b1c785d4c3ffef07ad71cfa979491f2443ae9d1e97b0fe555fa789bc5 b70b031136302efcacad1fdc8c46736706b780b2a5e02a5375f9c77e61c8b99c
mpw-002_NPCM_reticle_overview.png
the same image scaled to fit 4096 px
2.1 MB 311088a2aca50cc33472a9a9acc9d2e6a5776d853c01f55ea70f374fc8cdbfb8ea63b403 b30bf3c854a57d0d6f7c91b44460e9b23ee1b0d829ea40278b3b8f88aa71ae80
mpw-002_NPCM_tiles.json
where every tile belongs, and its checksum
13.3 kB c573f1928d67de39c596156e05d8f65c94d85848c8cc9b0154b2d5fcf756f0b7c88eb42a b00518596b67f966fc79000450c84ec8734b5a7b5581e37aea5543fb311e7601
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 axi_dma_using_spinal 7576×107760, 0 2.2 MBf9744b02916ced2324b0f902483bac04d4c1c593ae93789f26bef9b115a71574
A2 wishbone_can 7576×107767576, 0 2.1 MB19b66bd6abaf3e317397795f0c3a3f587bda190c3670affe1911f52e99920ddb
A3 Analog-Neural-Network 7576×1077615152, 0 1.3 MB602c5636ce7575d212150c20355d7349de1255e376bfe3840d789a665fc7cc42
A4 caravel_dsp2 7576×1077622728, 0 1.8 MBa0d5b8635aa454c1705c4d16a591101e78407d97312b7068640560cb7d7f5c0e
A5 eFPGA_v3_caravel 7576×1077630304, 0 4.8 MB7c28ea64b9ff8034e78bcc4d272ff18c48ac9902c7a289aea2abb9ef0698c750
A6 opencryo_testchip 7576×1077637880, 0 1.3 MBfabd2b153213b6ba82ee03e9756391669245394d6ce5e547bcaa984f70c0f450
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 2.6 MB6d1f9459358776ac9292b4557eca3ada7f1fde238a51e612afebaeed3e3c6e03
B2 lexicon 7576×107767576, 10776 2.7 MB440e463d34f4d3da4d4bee72774affb07442887cd7d9bd9252b88fb6d69c1ab0
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 2.3 MB3e210afdd3345e7a4f1517df8915ff9db810e6cea2d3aa4d7c4b42a42c89ebcd
B4 yonga-lz4_decoder 7576×1077622728, 10776 1.6 MB58e15d55a24326bfaa5767be430da69508270dad8e574c524dff0727ee1775f6
B5 Potentiometric_Digit 7576×1077630304, 10776 1.3 MBa456bc3019549ccce92bca1d8e583765a2990999adf8ea7968ac6c11e315a6a2
B6 fuserisc_ver2 7576×1077637880, 10776 4.7 MB12cc334662fcb354404d53b6d05c82db1b84871c230e124244dedfb1bb429360
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 1.5 MBa3988e8ef62217e4a8acd6eb201d7d9b29ecc7ae71cfa43592cc937768390def
C1 Comparator_3v3 7576×107760, 21552 1.3 MB48ffe187b369d0864d28b59b685c51d3036cd8b3578b66f258359f7a973f2381
C2 caravel_multi_encode 7576×107767576, 21552 1.6 MB0b6b60e7a0bbe1c8766f7eab357ff299e58f4853081fc55779972123349f53d5
C3 kasirgalabs_uart 7576×1077615152, 21552 1.5 MBda19ac14ac82068973b22907fe7e14116826e07b49a90ee82a3fa840492749d4
C4 Automatically_genera 7576×1077622728, 21552 1.4 MBc1bb1c43923cc4569d4c0829865b75769d756146c8994d5c5ecebdcfdfb4155a
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 4.0 MB05ad9d726f2b1b7ea99aa93df71e4dc5b4484dfcd34b2a26865238cfae6e5600
C7 caravel_azadi_soc 7976×1077645456, 21552 3.0 MB37e27cc5a067167dbef224a95393100a27c00536ece3b3dcc44f23eb62a666bd
D1 caravel_dsc 7576×107760, 32328 1.9 MB3fb981aafdc54d0c2caac003cbb4829c458162034ab7dd5547fcacf5d00a5299
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 3.2 MB4fc2f7f78a3faed71a220e60040800d440536ae5b39d6da2d4d94a1c6d04892a
D3 caravel_user_project 7576×1077615152, 32328 1.5 MB72fdb4827deb9dfe03dc7531a7f2fb3c6a2c244e0340f2bcaefbaf12bc0bb936
D4 General_Purpose_Band 7576×1077622728, 32328 1.4 MB35d954dcfb67ac5636c0631fc198f1864c9b61b9de2c4984d752ec00328f6fbd
D5 zero_to_asic 7576×1077630304, 32328 2.1 MB6a5e471b7fce51b3fabe7967a7a06f192d65a2be58a169ac209fa46c5048dd00
D6 fast_gcd_for_large_i 7576×1077637880, 32328 6.0 MBbe5ee87bcc888dfa0c9a8a448d5e3185770cad57acc7e41eae01512e5b3dbb17
D7 Columbus 7976×1077645456, 32328 1.3 MB9ee9f2c84d148dc66d70746aa6a4a9febc96cb614f2f8bd98f68513f45ceeeda
E1 Space_shuttle 7576×107760, 43104 2.5 MB3f2d51afabf96a4bfb3c5a2cb32fed0ea3ea565f46fd632c6e8233b43df5fdf6
E2 subservient 7576×107767576, 43104 1.7 MBa8f94066cff8e5ec0079d440c06e4c0f05f07baefe02b7e6c63cd7546c3ceb8f
E3 caravel_analog_fulgor 7576×1077615152, 43104 1.3 MBebd5baef76651ff0abb2f905df7b3cf1501a76dd4b36ea02eab91ce5ed2a144e
E4 bandgap_reference_de 7576×1077622728, 43104 1.4 MBeeb7903bbee15a2ec7014bb9c2d7b7808b2b2d5993783cb2c8f513b80eca2b6c
E5 renml 7576×1077630304, 43104 3.7 MBbb4e32c9836fde3a54d1598f115e324e5f380b2809f0b98b00dc60d9c5c02f59
E6 io_expander 7576×1077637880, 43104 1.4 MBa9f7faa185a8b452b67ff12132085b5acef013415639aca3603e1da270b439a5
E7 digital_pll 7976×1077645456, 43104 1.4 MB6ad92fa9b99c8a09ab22f270f8b2867165b7c854c28fc9f696f5c738887ca2ce
F1 zero_to_asic 7576×111760, 53880 2.1 MBf3ad4d0e4e2fc39da3180048f4fa3d7c33ce1f105a7e6821359368a827a6047e
F2 caravel_fpu 7576×111767576, 53880 1.7 MB8ab8bedb7ae89ac0e4f9ffed6b2e67b7246ce0bf4a950ec2f68f61306f9d5951
F3 BrqRV_EB1 7576×1117615152, 53880 4.4 MBebe84e5fddb73574e4bbf01ecc0ddb0e96dbfb77cf16bb903e465309194b53a8
F4 hilas 7576×1117622728, 53880 1.3 MB81014fd58e5abb28934ca91a6deaeb5be2a8aca719261439ab207d5d913ad299
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 1.3 MBee284c32d19adb9d06e94b888d135375c4570e086c0590dbea3512a69fae4b69
F6 sha1_engine 7576×1117637880, 53880 1.8 MBec28ca7bd47865c0083f05f1225947605158eb641af6be509af191b4216bf27f
F7 caravan_openfasoc 7976×1117645456, 53880 2.2 MBcc459014c9fb28e2f343803ccbc16ea43d5e1df939fa3c0a286853411b546be5

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 NPCM on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask NPCM

Mask layer
NPCM — Nitride Poly Cut
Process step
78 — Nitride poly cut mask
Drawn layer
95:20
Layer on the mask-layer GDS
49:0
Used in SKY130
yes

mpw-002 — SKY130 MPW-2

Foundry product
EF MPW#2
Reticle set
5CS8007AC
Run date
2021-06-18
Submissions closed
2021-06-18
Projects
56
Fab lot
4205819
Identification
confirmed — Lot records: EF MPW#2, lot 4205819 = 'SKY130 MPW 2'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-2 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-002/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.