mpw-002 / MM5 Metal 5

the MM5 shapes of all of mpw-002

40 of 40 dies rendered.

9,757,751 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 (14.2 MB in total, largest 473.1 kB), 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_MM5_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_MM5_reticle.png
the stitched reticle, full resolution
8.8 MB e24005f0a22d97b68adea9cd9638938ac2dc1f9afad5aef78617a00d39a583231268b842 8556e9ab44c69a6f90115c62bc23ad8367c02dbc0b326f74971b2cfaa108ec2b
mpw-002_MM5_reticle_overview.png
the same image scaled to fit 4096 px
4.4 MB 203c894f15b4e7ac1aaa7df258b21170780916201bf4ba11b34d31d368d58e3cdc11201d 8a46444beadeeb09000ef43a13d02862cbeed9ca2948ac9e80a209efaec70ef1
mpw-002_MM5_tiles.json
where every tile belongs, and its checksum
13.2 kB 557f173ea77eb77a2bce882c820b26d88e545bfd6b3249c697bf391bb53a1eefc1343b7b 218ba5ea9322e49874ed5223a883452d868f9ec31529f8833bfe4f9b803f378d
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, 0 344.7 kBff6ecf60f3048b187392b5a96e0136fbc5418c684ed1c210760d52f70580df43
A2 wishbone_can 7576×107767576, 0 345.8 kBf7b8f154a1530ebc52fa6a299dba3840ebdc7de056de7ad5f802b9b478f813a5
A3 Analog-Neural-Network 7576×1077615152, 0 351.8 kB0b7dbee90ab91e7d4f806bd7cf520501eae6fc191b1f800b3afb20c2c1daad80
A4 caravel_dsp2 7576×1077622728, 0 343.9 kB692241e729f9fdb40b0b4e8bdc4fcd1d63259b6ba66bee295d327c0e2d3b3826
A5 eFPGA_v3_caravel 7576×1077630304, 0 328.1 kB84f6236c3f1183cbf521fb010dad908a20cbc73aa6ab6ac911f9d1a7bc8a7db4
A6 opencryo_testchip 7576×1077637880, 0 352.9 kBf84308b13acfab5911bba4139695439517e352e4325924ae24039ee6fa881d8b
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 346.5 kBbc19eb6ea360ad3e638792a780cbf3725ffd38049715ddae96fabd377f726b08
B2 lexicon 7576×107767576, 10776 339.2 kB28945ce1bc720667cd9fc28b931724373a9efbca74ed37dc8cbfef127788732d
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 328.2 kB8192a039a65ffd9c1bcfdaea59562f877840a033ee17885c31ca33599e824c61
B4 yonga-lz4_decoder 7576×1077622728, 10776 344.1 kB28944d3470b51132947bc029d342e4fc1f58a0c1e547d489d5d392507d5b0675
B5 Potentiometric_Digit 7576×1077630304, 10776 351.8 kBc9d7c332ffb4e626a59793a23d7a1c443353960e286dad70ef69c1be0c42f016
B6 fuserisc_ver2 7576×1077637880, 10776 328.1 kB2dbd4ca3962391c81d31f8aad11b719fc3204e6a0783120fc223a064b8d013da
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 387.2 kBaebaf7b17a7bc606673eee7fe80eee75f6246eac044391ff6bea35c195175875
C1 Comparator_3v3 7576×107760, 21552 351.8 kB9173cae4aa6c63af3efdcdfa93f3f9d6e182f56ec098969fcb9526209924e850
C2 caravel_multi_encode 7576×107767576, 21552 344.3 kB3889790b1706c54e49cd6e1400b34fa562804ed76fd0a4b68f1610c34f454452
C3 kasirgalabs_uart 7576×1077615152, 21552 343.4 kBa4d7508f1dd21b6b328bc7efaa97cd67923de476b2188f2c489829cc3932a568
C4 Automatically_genera 7576×1077622728, 21552 342.5 kB60ebd495722a6103b96d7e541cc010cf485d395ab0a12818aba2a454c9821baa
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 338.8 kB8cc7e28bf95fd7e82e34b7b3ff6532c4fc3ac96c26c8c1f6f7a13b4bc4f13f5e
C7 caravel_azadi_soc 7976×1077645456, 21552 346.2 kB180eb13860781436a14a8610de833cf5a31e7e475136d78e90eaaa1fdefe9974
D1 caravel_dsc 7576×107760, 32328 344.2 kB8587880627663506d89431f9562e16185718b03c65586208b1c974b9766bc060
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 346.4 kB191833099bc306bd5f35b01ab533deb89ffd038902afe0ba6d13d2f400a28146
D3 caravel_user_project 7576×1077615152, 32328 343.4 kB3d899248e8e748121fb3d794a30a0d68dfbd3edd9102a4955aa1d6d4374e6445
D4 General_Purpose_Band 7576×1077622728, 32328 342.4 kB59eca0244ecf8a0ba11a000a1c96ec56c82447f23862750564152c9d56216ef6
D5 zero_to_asic 7576×1077630304, 32328 344.1 kB112b407ac09fbd1b72afe897f92e172e7ac21675b365c8e8014ff9a652d898d4
D6 fast_gcd_for_large_i 7576×1077637880, 32328 473.1 kB7f55c0dc81e3cfeceb6b760344c5c017d3c6b7dd2020bc71dea3b47dd3ba5658
D7 Columbus 7976×1077645456, 32328 354.6 kBaa6aadc4b9db0eeb503f3f9fab4e2d969ac2221554e3367195784719feb51d26
E1 Space_shuttle 7576×107760, 43104 345.5 kB3f121eed7359e7c83263d86bb2184d560ba9d65ed47eae97a34bc8a44862f995
E2 subservient 7576×107767576, 43104 342.4 kBc99b226e9208af620dcd1cd2a679f416c7670ae07fbc5f61109b85e48453e465
E3 caravel_analog_fulgor 7576×1077615152, 43104 394.5 kBcd23250b9d60be0dc0726fbda7974a2e33798c4d7c3effa1b7fba9c08fa21aa8
E4 bandgap_reference_de 7576×1077622728, 43104 342.4 kB3d82eb6ef91694176c715ca943b7c76035e19f7215eaa6c03c64352fbfa2c9d3
E5 renml 7576×1077630304, 43104 341.4 kB88c498667b6919e4e773a4a606efc7a2f471cadf04a9620c538d70ce40790e65
E6 io_expander 7576×1077637880, 43104 342.5 kB3d6c7ce9847570febaf4d78029143dc2ce3d4f107872f50e1c8907a99ab0e393
E7 digital_pll 7976×1077645456, 43104 359.0 kB7d6f34d436480ba1df5addcb53d9f369581588a5e3fe10936175487205eaa723
F1 zero_to_asic 7576×111760, 53880 348.0 kBb2babb7a44e1d4768cec5ed46243024f9690f759df8146bda9f2edc35fb9c696
F2 caravel_fpu 7576×111767576, 53880 346.5 kB2afb432eb2cbdca6563dca46ab1d05219ddd5caebbb292e817615af48f81d066
F3 BrqRV_EB1 7576×1117615152, 53880 342.7 kBd46b6007ddf4da2798fecf0da6506308d993a076816f78b04d5355877b8b13da
F4 hilas 7576×1117622728, 53880 355.8 kB5137005c052536e40845b2197665e4713a04c7d4ab103acae05f6b32b213642f
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 355.8 kB764a7507e749addd07ba126533b38818c18546fa936bfc5ecfbbe9822f408fe6
F6 sha1_engine 7576×1117637880, 53880 347.7 kB825cdedc133472cd5e3a7a1da3469833816a638e31c3a6d57a36021966b8e3a2
F7 caravan_openfasoc 7976×1117645456, 53880 367.4 kB61724b77ae8dfd888ce5d31c25c0d9745dd87388beba184ef94eda2944f80bb1

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 MM5 on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask MM5

Mask layer
MM5 — Metal 5
Process step
162 — Metal5 mask
Drawn layer
72:20
Layer on the mask-layer GDS
59:0
Fill layer
59:28
Also
Metal 5
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.