mpw-007 / MM5 Metal 5

the MM5 shapes of all of mpw-007

40 of 40 dies rendered.

9,859,452 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 713.8 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-007_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-007_MM5_reticle.png
the stitched reticle, full resolution
8.8 MB 7603cd837989fc68d4a27a47464f48bc90bc4c5b7af80afefa5e53af596106b8044676c3 f6813881d7b5bf8f91824e3804202260bee543f0c4c8d847ea838a15b6e6ffdb
mpw-007_MM5_reticle_overview.png
the same image scaled to fit 4096 px
4.3 MB 6fc6a4f6680d66d0d6dfdcc1a075e336a715a8d095593b1b19f0699b85c7d743b9aa68d1 b72af76e8dc4ca1b33347cde2b86cc751acd35b9a009b211cadc83bebe511986
mpw-007_MM5_tiles.json
where every tile belongs, and its checksum
13.5 kB f993942da68dcb65db87563fe1c62f19902844775f1a0268f2e209ff3bbe6136315f7649 68eaf2c40ffda55e05410cd60dbb961655dd0487b986d564b25dfbb67dfb3c38
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 tinytapeout 7576×107760, 0 332.5 kB66aacf7798f0e0a65b87eeb96f54b09d1b0aa4793c1cd82d30e21326de5d6910
A2 zero_to_asic_course_group_submission_mpw7 7576×107767576, 0 307.4 kBd0519216ef0141421793ea91f6db6dd8ecdf9fa4d3cc23f4e1d98e2f0faced26
A3 microwatt_mpw7 7576×1077615152, 0 320.0 kB51591492ac4341cde55af833db6286a01d00a3c5b49e7fafd50677c57cf77111
A4 yonga-can_controller 7576×1077622728, 0 317.4 kB0c71e41c0f16c6c47a8729b255c8f8ff58b7252aa900811a676ebcc173e4c679
A5 rocketalpha 7576×1077630304, 0 347.0 kB6a768571c36ea2623e57044293c0ac4d72210ddbcfccfebc59663c5756977513
A6 riscduino-dcore_d3_3 7576×1077637880, 0 306.7 kB32ad59c0a96892997bbc7261678f568b81d9b334d06f58c2e38de32edf32934a
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 riscduino-qcore_q2_3 7576×107760, 10776 306.8 kB28380814ee41eb2f3eb6d47437737c3415c8e5a7a092747548773e5b17276ba0
B2 riscduino-score-s4-3 7576×107767576, 10776 306.8 kBe52b3171fed0c2d664e44db8e3957330ee23f511446aee0a248435c1296b9b7d
B3 nanofabrication_project_using_openfasoc 7576×1077615152, 10776 486.5 kBbf0e449fcb42bbb9968ca5ffebebb1ebab28c4c7cc3f1154634ef370e609474b
B4 sramtestvehicleg3 7576×1077622728, 10776 349.2 kB38c39c4ba76b90b47be4d203d2e81be8b7256e8d72104ba665b5453bc4126026
B5 analog_frontend_for_particle_detection_resubm 7576×1077630304, 10776 370.2 kB3f70346767b8d8443b29ba731be4ef0d43445aba1cfc56a98bd54e825a449f67
B6 isa_16-bit_microprocessor 7576×1077637880, 10776 307.4 kBcd3a693f7936f44db2ab6b7012a1e3196fd3d08e10d3de5ad4af6115dfd49b3d
B7 reram_crossbar 7976×1077645456, 10776 713.8 kB64661d4fd65dec9a5afea901cb41faeee00b2b9d0ae52d83d671b6f88149d264
C1 trainable-nn 7576×107760, 21552 317.4 kB1023df358f42a0ade0558162175f501871456c8c898100999ed12bf4d783719b
C2 10b_adc_and_analog_support_-_update 7576×107767576, 21552 376.9 kB84edb24f408f0fe66cbfd02818300b7c20ae728ac4f047a2626d90f5f8a7dbbf
C3 bitcoin_mining_asics 7576×1077615152, 21552 307.3 kB9f6d4af15f69ef62b8879941a8924e5cd3895e0eb40258e70181f59b9da74317
C4 yonga-mcu 7576×1077622728, 21552 307.3 kB6e45937dc1418e5fd27874ea4db50bdf508292d7c87b39fa669bcea22836024a
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 soomrv 7576×1077637880, 21552 317.3 kBac26488d5f5cb004604f4ee09364a9b360aa0846a3882ad6a188f17e8675a329
C7 marmot_risc-v_asic_for_motor_control 7976×1077645456, 21552 334.0 kB25cf3666fadc2964460ea17acd8ec2f8d9226d0051e1e6a4cafabba696df3dda
D1 mixed_signal_circuits-jun13 7576×107760, 32328 349.1 kB1a4526019d66909a72f4823b80b68b059f725f808fe444a0ff85c9ed008d00b4
D2 fpga_programming_management_unit 7576×107767576, 32328 331.2 kBb2cb33793b7c465debd7e51be5bad2a8de721135c0e68439809110e020446096
D3 warp-v 7576×1077615152, 32328 307.4 kB4a7c855e44c29f39d4c751744909943646c195ea7d75a2171d76fff3025272d8
D4 leaf__mpw7_ 7576×1077622728, 32328 324.6 kBab2205c599b332017ab0003e7b5cad33a1e303dd9a62e7e8ae1a9210e227165a
D5 in_memory_computing_sram 7576×1077630304, 32328 320.0 kB73a8a67f1368a48ae7bfb0f03e6e1cf92c3f82c474251f95545c1ab6ba473ac3
D6 rng_based_on_a_figaro_oscillator 7576×1077637880, 32328 317.4 kBb8c4f69100037add575594383a020ebf2e205ce336321c7aa7ccee15d7e330c5
D7 reram-controller-mpw7_v2 7976×1077645456, 32328 362.0 kB38058142311eff21cccfb53084a3a368f90b13f777ea6478411eb0c9196dcce0
E1 enhanced_chaotic_oscillator_design 7576×107760, 43104 307.4 kBb57a8f4470b9b2e3a15c69df413a3eb22f39d3e86eba6a6b549f7331070cc416
E2 in_memory_computing_rram 7576×107767576, 43104 337.0 kB189c3a83b9cac9572a3eee8dd08078df6f93378c71b19f991c91ca67962ce083
E3 rram_imc_v2_flat 7576×1077615152, 43104 337.0 kBd07816e1244667c3dd1b1d554b662ae2204b2595f993dadd244117e55f9b8150
E4 topmetalse-dps 7576×1077622728, 43104 449.4 kBa9e5a5ab463b8d7b95264770f267a7256e72b306a2e036053cfef599ef885df6
E5 chaos_automaton 7576×1077630304, 43104 335.1 kB0cb5e2d390fba14ec8ac302bcdd5ec810a5bd32949e630ade1057c85f15fb484
E6 hehecore_resubmission 7576×1077637880, 43104 314.6 kBf0c4664d62109e50b946e95bab03179e7799b0fb9ea23a6269796fbfacff9a88
E7 waveform_generator 7976×1077645456, 43104 321.4 kB0a926ce89870d045b15ad1e5e12daeef46a90b42bac747afe132eaab97d94898
F1 rift2core 7576×111760, 53880 321.6 kB9603aa3c27e6a0865061b237a063522e6445e54ab7615ebc1df1fcffb8d99fa5
F2 rift2go_2300 7576×111767576, 53880 321.5 kB354743d642d6497466e034f07f827d7f2f6a7059dded899baa627201446894a6
F3 sloci_resubmission 7576×1117615152, 53880 355.9 kB9240d4753cec10764ba78b946d4b88a8110aedf4b07a0c6f86a232a85dc32b06
F4 systolic_array_matrix_multiplier 7576×1117622728, 53880 311.2 kB432cc89aada04e240ebe1acc584a91f1528093532ec0fcf6247158a6c2bc41cf
F5 crypto_aes128 7576×1117630304, 53880 311.3 kB0603f2bbb221b3ae5227dd1cbb60434cfbe0ea92b115e7d00d8f31e4ec715391
F6 graphics_controller 7576×1117637880, 53880 311.5 kBba69d4f421ef476cc5badb82d61aeabe29bb804bd61087097246cd9b92f8fdd6
F7 prga-test 7976×1117645456, 53880 332.7 kBc9c3b4f6923ff96c5d6724035b7fa2284cb213b5318a6971a2c157afd1636d8f

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-007

Resolved: reticle 5CS8016AC, run mpw-007, 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-007 — SKY130 MPW-7

Reticle set
5CS8016AC
Run date
2022-07-08
Submissions closed
2022-09-12
Projects
106
Identification
inferred — Sequence after 5CS8014AC=MPW-6 (8018 is the re-made MPW-4 set); packing list 2026-08-25
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-7 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-007/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.