mpw-008 / MM5 Metal 5

the MM5 shapes of all of mpw-008

40 of 40 dies rendered.

9,715,923 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 (13.9 MB in total, largest 709.7 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-008_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-008_MM5_reticle.png
the stitched reticle, full resolution
8.6 MB 07f416a508df56eb0b24a93d4106b7b99b2e0a3003bdeed246b0def83c12da87c6044f7b 27010d8775287101468b510f0f453d786a0d9647784e381fa103c64341eea688
mpw-008_MM5_reticle_overview.png
the same image scaled to fit 4096 px
3.8 MB e2ebe5191c609a3700e87fb1cdef3b5e51e7c2a4011e607ba65c950086934bb3c4b62442 6383852d740c013bf075905741940e37c69ac00b2821fa69cb9428d1104e4a1e
mpw-008_MM5_tiles.json
where every tile belongs, and its checksum
13.4 kB 9f4e428fae6555feede738a369a2e4d6fbc69a02be24565fccff3d7060bcddac8e2a72a4 f10f74a14c5776b8935eddd7015702cf88260e6efdc6313ef00c82ada6c2f80c
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, 0 317.5 kB92db461d08139f2b1a4bbae3bf98badbc83be1b403aa6cab56c7a3d5b7c3208c
A2 iit_indore_mm 7576×107767576, 0 317.4 kB9e5894c6e91df1a856c9e960a747abad09b965dafeea142d50fc3e2f4ea99dc8
A3 energy_efficient_sum-of-products_circuit 7576×1077615152, 0 317.3 kB896d219ee5378733c3152b34f46edb96a198ca2824a03d5ba65afa0229f3fb78
A4 riscduino-dcore_d4_ 7576×1077622728, 0 306.6 kB8d57e64e798382f3993acd337cd565d60bf89de1be292bf426f868e57feec96c
A5 riscduino-qcore_q3_ 7576×1077630304, 0 306.6 kB4e181ca0adaebd9d054101811849415549a9c2a18af0ab53d97be6527524119b
A6 caravel_mpw8 7576×1077637880, 0 317.4 kB43d622dfd5bbd9b9833f699d9f0b62690439f0147d7841969ae1617d56aa9a1a
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 nanofabrication_project_using_openfasoc_mpw8 7576×107760, 10776 545.6 kB9ab06674c3934be06ef711a14162457a6c4558ae056ffb07aa7cea073aba81fd
B2 sramtestvehicleg4 7576×107767576, 10776 349.2 kBaff05cc8476abc5a01827ea330e1877fb49d768ba8e219f6ce0b476c5b2dc45b
B3 reram_crossbar_rerun 7576×1077615152, 10776 709.7 kB2bf50f540f978d3b5722f25bc84acd3366a0ecc1e5f7d9e257ca9837b000e3d4
B4 tinytapeout_skullart 7576×1077622728, 10776 335.0 kB441c11abe2ccddb3e1899a6cabc28a9b6958cc4ef0d1ec6dc1dbb9032714e2c8
B5 soomrv__mpw-8_ 7576×1077630304, 10776 317.4 kB7e83387afec943c00d7391b53b65b084f861be4975b0a1d2e882ba0b94f7996c
B6 clock_divide_selector 7576×1077637880, 10776 317.5 kB50568a284d7139beadb0c2a668b6be33f060f234bdac16f2438a6e1da9f8f5b3
B7 reram_module_mpw8 7976×1077645456, 10776 362.2 kBf3b32e989c30d40130ee527e948a370c642afdb3fe3c9ce765c1f3a30dee33da
C1 shapingthewaves_8 7576×107760, 21552 310.9 kB7b2d467e7e320ce4561d9cf46411cd36e024aca344620487de09a7832418b80c
C2 leosoc 7576×107767576, 21552 317.4 kB63fa3ac7dcb6d4d180d8e8fb36974a895851bf6793b00380ec8a2c5a8e050d60
C3 decrypt_aes128 7576×1077615152, 21552 317.4 kBaf6f7f8016b745eac7e879608ad3fc177c1cd2f83cefa60fab5d806de1e65a8b
C4 approximate_adder_axha 7576×1077622728, 21552 317.4 kBe71f62e361713caed3d2264100fa96f616560cc1d1748eb5af7407faf8cc2c2d
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 riscduino-pxt1 7576×1077637880, 21552 323.1 kB1c6f522dbc583ad69ecfef6afb3ce38631baaa5536bf9549e071035e8874cef7
C7 fitbit_project_using_openfasoc 7976×1077645456, 21552 365.9 kBde7c449ab752c1cdf82c015754efb6603c9252cbf7a7b65547aea68978b74e46
D1 rift2go_2320_sky130_mpw8 7576×107760, 32328 317.4 kB4e9e61e9d2c7dfd6e07e0ca13cc84b713986cfa8ab15fa3b3f6d11c8a9b4741e
D2 hyperram_controller_for_sky130 7576×107767576, 32328 317.4 kBaa6ee92e8078e3e97eb3e7e172c0a78c83d37c33f84b70accfea53924295fa41
D3 hardened_alu 7576×1077615152, 32328 317.4 kB11d904d42964b6922cdd29181385701fafd382acfc27af2db609f9faca6f6d8a
D4 an_open_source_hitachi_sh2_for_motor_control_apps 7576×1077622728, 32328 333.4 kBcea6f57fb5119114bac093d18f5830e293e4b7e6a713987335958549094a674a
D5 mixed_signal_circuits_mpw8_dec14 7576×1077630304, 32328 349.2 kBbcc1b84964ddf250fe429f012a5358cb3a5634d336c8b20c5de440aeaf056c9c
D6 gf180_vs_sky130 7576×1077637880, 32328 317.5 kB0a3c410140ef3b092adbe8a828dbe1857a6c9487f00a8bf863cef052ecd5c773
D7 iiitb_tlc_mpw8 7976×1077645456, 32328 321.3 kB0bc24f2055ba3d7cfd15c445c002eb8899187f225339ae7058a39734ff448137
E1 iiitb_sd_mpw8 7576×107760, 43104 317.4 kB78752d9c43136a7af491bb47fbc019b4a874fad227cb998144ac41ffd04dc427
E2 iiitb_sfifo 7576×107767576, 43104 317.3 kB45e387596afeb641060cb73f2259630eef6f38e21b8f7bd882ab0a64366b0a5b
E3 iiitb_alu 7576×1077615152, 43104 317.3 kBa2dfc141157d168a20e08cad71f3bdfcb82d1e9e12d1963ac80c0354227c9b9f
E4 ppcpu2 7576×1077622728, 43104 317.4 kBfe5e0fbff912f35003ce8974d589edf157c24468eae08a8a56b0a608183b4494
E5 mega4_reram 7576×1077630304, 43104 454.1 kB3692135b21971f63508284ab08ff76b0ba3651aa4eaef02f632963183312ad4c
E6 tiny_user_project_vgaclock_mpw8 7576×1077637880, 43104 317.5 kB985a16f16fa557917747b5e9bfca8c8a8e3f9bc73c011848b3a4456ba931e2ec
E7 sky130cla 7976×1077645456, 43104 321.4 kB2cee5f75caa45085ea491d51807f4fa5de02ee3ba316ca499aebfc1551a9c042
F1 uetrv_esoc_v2 7576×111760, 53880 321.5 kB10b66bbc8ffbee4cadb8214e4a19b2ab50d42c3d211caca8d90de3a90ea9cfd3
F2 sequencedetection1010 7576×111767576, 53880 321.6 kBe03f912f601d86afad99527a5b03b24d76784868c8ef92c42146382070381b2d
F3 testasynctrimux-mpw8 7576×1117615152, 53880 321.5 kB092b6cad7c99966e44e307e340bcbc902b466481b91b2b162495f2fb8caa2ad8
F4 iit_indore_dadda_multiplier_mpw8 7576×1117622728, 53880 321.5 kBabe36f3870d3907da043b0d714de836ac5a3730ebbd15f6873391862547a7a39
F5 modified_baugh_wooley_multiplier 7576×1117630304, 53880 321.5 kB7c4bb313afcc63faf68934e379f1a8bf1728624dabbb7725a25172c74a644fd0
F6 arbiter_puf 7576×1117637880, 53880 321.6 kBefd2a9a7139de2e45b5e0855b006dd86ec507f9457b4b11ec7c71e0917a25056
F7 unigate-sky 7976×1117645456, 53880 325.4 kB6e52d1a4967055afb2bab162c4221f29f0d9750a28af2e1f0bb1f60451985179

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-008

Resolved: reticle 5CS8017AC, run mpw-008, 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-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.